Triple
T27463662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Place Louis-Lépine |
E693118
|
entity |
| Predicate | hasNearbyTransportStation |
P160825
|
FINISHED |
| Object | Cité metro station |
—
|
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: Cité metro station | Statement: [Place Louis-Lépine, hasNearbyTransportStation, Cité metro station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyTransportStation Context triple: [Place Louis-Lépine, hasNearbyTransportStation, Cité metro station]
-
A.
hasNearbyRailwayStation
Indicates that a railway station is located within a short or convenient distance from the referenced entity.
-
B.
hasTransportationProximityTo
chosen
Indicates that one entity is located near or conveniently accessible to another entity in terms of transportation options or routes.
-
C.
hasNearbyTramStop
Indicates that a location has a tram stop situated within a short walking distance or close proximity.
-
D.
operatorOfNearbyTransit
Indicates that an entity operates or manages a public transit service located in close geographic proximity to another specified entity.
-
E.
hasPublicTransportStop
Indicates that a location or area contains or is served by a public transport stop, such as a bus, tram, or train stop.
- F. None of above.
Provenance (3 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_69ef538105548190a771cc5a0cf8c211 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69fcf825ca7081909d06b0df33eb33f9 |
completed | May 7, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69fcf42160f0819096812a8bf590875e |
completed | May 7, 2026, 8:20 p.m. |
Created at: April 27, 2026, 12:51 p.m.