Triple
T7852988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 149th Street–Grand Concourse station |
E182101
|
entity |
| Predicate | isExpressStop |
P25018
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [149th Street–Grand Concourse station, isExpressStop, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isExpressStop Context triple: [149th Street–Grand Concourse station, isExpressStop, yes]
-
A.
hasStopType
chosen
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
B.
canExpress
Indicates that an entity has the ability or capacity to convey, articulate, or communicate something (such as an idea, emotion, or property).
-
C.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
D.
hasStopNear
Indicates that one entity has a stop or stopping point located in close proximity to another entity.
-
E.
isNonstopPossible
Indicates that it is possible to perform or complete the referenced trip, route, or process without any intermediate stops or interruptions.
- 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_69ca82869ee08190b8f9040dbc2c0467 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb18ed56d481909266d862e0ae152d |
completed | March 31, 2026, 12:44 a.m. |
| PD | Predicate disambiguation | batch_69cae92180f88190ae3d44c3de7adc93 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:51 p.m.