Triple
T10348354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro Hidalgo station |
E243812
|
entity |
| Predicate | connectsLine |
P845
|
FINISHED |
| Object |
Line 2 (Blue Line)
Line 2 (Blue Line) is one of the main lines of the Mexico City Metro system, running on a north–south axis through several key central and residential areas.
|
E857749
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Line 2 (Blue Line) | Statement: [Metro Hidalgo station, connectsLine, Line 2 (Blue Line)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 2 (Blue Line) Context triple: [Metro Hidalgo station, connectsLine, Line 2 (Blue Line)]
-
A.
Line 2–Green
Line 2–Green is a major rapid transit line of the São Paulo Metro system, serving key central and eastern districts of São Paulo, Brazil.
-
B.
Line 2
Line 2 is a major route of the Tunis Metro light rail network, serving key districts within the Tunis metropolitan area.
-
C.
Line 2
Line 2 is one of the two automated light metro lines of the Lille Metro system in northern France, serving numerous stations across the metropolitan area.
-
D.
Line 2
Line 2 is a major east–west rapid transit route of the Shanghai Metro that connects key commercial, residential, and airport hubs across the city.
-
E.
Line 2
Line 2 is a major east–west rapid transit route of the Nanjing Metro system in Nanjing, China, connecting key urban districts and transportation hubs.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Line 2 (Blue Line) Triple: [Metro Hidalgo station, connectsLine, Line 2 (Blue Line)]
Generated description
Line 2 (Blue Line) is one of the main lines of the Mexico City Metro system, running on a north–south axis through several key central and residential areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 2 (Blue Line) Target entity description: Line 2 (Blue Line) is one of the main lines of the Mexico City Metro system, running on a north–south axis through several key central and residential areas.
-
A.
Line 2–Green
Line 2–Green is a major rapid transit line of the São Paulo Metro system, serving key central and eastern districts of São Paulo, Brazil.
-
B.
Line 2
Line 2 is a major route of the Tunis Metro light rail network, serving key districts within the Tunis metropolitan area.
-
C.
Line 2
Line 2 is one of the two automated light metro lines of the Lille Metro system in northern France, serving numerous stations across the metropolitan area.
-
D.
Line 2
Line 2 is a major east–west rapid transit route of the Shanghai Metro that connects key commercial, residential, and airport hubs across the city.
-
E.
Line 2
Line 2 is a trolleybus route within Geneva’s public transport system that serves as one of the city’s main electric bus lines.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e945d51881908dd2af6c78344c9b |
completed | April 7, 2026, 11:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7508e325c8190a88c2b972f8a6846 |
completed | April 9, 2026, 7:09 a.m. |
| NEDg | Description generation | batch_69d7618da0188190901026dd51ceaa46 |
completed | April 9, 2026, 8:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d77045ea988190bd8e31f5f636f69b |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 6, 2026, 11:56 a.m.