Triple
T31780022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevilly-Larue |
E811174
|
entity |
| Predicate | nearTramwayLine |
P172293
|
FINISHED |
| Object | Île-de-France tramway Line T7 |
—
|
NE NERFINISHED |
How this triple was built (2 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: Île-de-France tramway Line T7 | Statement: [Chevilly-Larue, nearTramwayLine, Île-de-France tramway Line T7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearTramwayLine Context triple: [Chevilly-Larue, nearTramwayLine, Île-de-France tramway Line T7]
-
A.
nearestRailwayLine
Indicates that one railway line is the closest in distance to a given location or feature compared to all other railway lines.
-
B.
nearestCommuterRailLine
Indicates the commuter rail line that is geographically closest to a given location or entity.
-
C.
hasNearbyTramStop
Indicates that a location has a tram stop situated within a short walking distance or close proximity.
-
D.
nearUndergroundLine
Indicates that one entity is located close in distance to an underground (subway/metro) line.
-
E.
nearestRailwayTerminus
Indicates that one location is the closest railway terminus to another specified place.
- F. None of above. chosen
Provenance (4 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_69f348e544a48190ab6e700b05f6438c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6abe4b214819082e76060f09b4a27 |
completed | May 3, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69f6aa21f2508190a204a424ffc00ca6 |
completed | May 3, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69f6aac95c1481909fff33702d0a6c37 |
completed | May 3, 2026, 1:54 a.m. |
Created at: April 30, 2026, 11:36 p.m.