Triple
T3817677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prague Metro |
E84295
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
Line A
Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
|
E390322
|
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 A | Statement: [Prague Metro, hasLine, Line A]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line A Context triple: [Prague Metro, hasLine, Line A]
-
A.
Line A
Line A is a line of the Mexico City Metro system that serves the eastern part of the metropolitan area, connecting central Mexico City with several suburban municipalities.
-
B.
Line A
Line A is the historic first subway line of the Buenos Aires Underground, known for its early 20th-century wooden cars and route through central neighborhoods.
-
C.
Line A
Line A is one of the main tram lines serving the city of Reims, France, providing urban public transportation across key districts.
-
D.
Line A
Line A is one of the main routes of the Strasbourg tramway network, providing key light-rail transit across the city.
-
E.
Line B
Line B is one of the main tram routes in the Reims tramway network in Reims, France, providing urban public transport across key areas of the city.
- 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 A Triple: [Prague Metro, hasLine, Line A]
Generated description
Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line A Target entity description: Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
-
A.
Line A
Line A is a line of the Mexico City Metro system that serves the eastern part of the metropolitan area, connecting central Mexico City with several suburban municipalities.
-
B.
Line A
Line A is the historic first subway line of the Buenos Aires Underground, known for its early 20th-century wooden cars and route through central neighborhoods.
-
C.
Line A
Line A is one of the main tram lines serving the city of Reims, France, providing urban public transportation across key districts.
-
D.
Line A
Line A is one of the main routes of the Strasbourg tramway network, providing key light-rail transit across the city.
-
E.
Line B
Line B is a major Mexico City Metro route that runs diagonally across the city, connecting central areas with northeastern suburbs and serving as an important commuter corridor.
- 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_69aed931f5908190be2c07af66d4df25 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee8e1852c8190b61c507a7128d4c6 |
completed | March 9, 2026, 3:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4fb43e1d481909a5b52cae5686179 |
completed | March 14, 2026, 6:08 a.m. |
| NEDg | Description generation | batch_69b4fcec1cf48190aa0b128acb56c089 |
completed | March 14, 2026, 6:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4fd4d55248190bf4ef442a9991edf |
completed | March 14, 2026, 6:16 a.m. |
Created at: March 9, 2026, 3:17 p.m.