Triple
T5074183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lille Metro |
E114351
|
entity |
| Predicate | line |
P1293
|
FINISHED |
| Object |
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.
|
E496538
|
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 | Statement: [Lille Metro, line, Line 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 2 Context triple: [Lille Metro, line, Line 2]
-
A.
Line 2
Line 2 is one of the main lines of the Santiago Metro in Chile, running in a generally north–south direction and serving several central and densely populated areas of the city.
-
B.
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.
-
C.
Line 2
Line 2 is a planned second rapid transit line of the Turin Metro system in Turin, Italy, intended to expand the city's urban rail network.
-
D.
Line 2
Line 2 is a major circular line of the Seoul Metropolitan Subway system, known for being one of the busiest and most important routes in the network.
-
E.
Line 2
Line 2 is a major rapid transit route of the STC Metro system, serving key districts along one of the network’s primary corridors.
- 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 Triple: [Lille Metro, line, Line 2]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 2 Target entity description: 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.
-
A.
Line 2
Line 2 is a circular line of the Brussels Metro system that serves central and surrounding districts of the Belgian capital.
-
B.
Line 2
Line 2 is a rapid transit line of the Barcelona Metro system that serves several central and northern neighborhoods of the city.
-
C.
Line 2
Line 2 is a major route of the Tunis Metro light rail network, serving key districts within the Tunis metropolitan area.
-
D.
Line 2
Line 2 is a major rapid transit route of the STC Metro system, serving key districts along one of the network’s primary corridors.
-
E.
Line 2
Line 2 is a major circular line of the Seoul Metropolitan Subway system, known for being one of the busiest and most important routes in the network.
- 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_69bd443cf28c8190ad371d603563dbdd |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74d0be1c819081b26235fe602a30 |
completed | March 20, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec35e7b848190bbb4cea9d09531e0 |
completed | March 21, 2026, 4:12 p.m. |
| NEDg | Description generation | batch_69bec505a5dc81908f79c1ade107c4ce |
completed | March 21, 2026, 4:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bec654fc4881909bf5458cdafc7ffd |
completed | March 21, 2026, 4:24 p.m. |
Created at: March 20, 2026, 1:39 p.m.