Triple
T11058687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L1-SL |
E261446
|
entity |
| Predicate | refersToStopType |
P25018
|
FINISHED |
| Object | metro station |
—
|
LITERAL FINISHED |
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: metro station | Statement: [L1-SL, refersToStopType, metro station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToStopType Context triple: [L1-SL, refersToStopType, metro station]
-
A.
hasStopType
chosen
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
B.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
C.
hasStopNear
Indicates that one entity has a stop or stopping point located in close proximity to another entity.
-
D.
appliesToTransportFacilityType
Indicates that something is relevant or applicable specifically to a particular type or category of transport facility.
-
E.
stopsAtStation
Indicates that a vehicle or service halts at a particular station as part of its route or schedule.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798a4f3f88190a29710f64cef9d25 |
completed | April 9, 2026, 12:16 p.m. |
| PD | Predicate disambiguation | batch_69d7440da46c8190a77380d5d747ac9c |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.