Triple
T33605495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pulse Harlem Line |
E860839
|
entity |
| Predicate | hasStopStyle |
P199751
|
FINISHED |
| Object | bus rapid transit stations |
—
|
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: bus rapid transit stations | Statement: [Pulse Harlem Line, hasStopStyle, bus rapid transit stations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStopStyle Context triple: [Pulse Harlem Line, hasStopStyle, bus rapid transit stations]
-
A.
hasStop
Indicates that something (such as a route, service, or journey) includes or is associated with a particular stop or stopping point.
-
B.
hasStopFeature
Indicates that one entity possesses or is equipped with a feature that enables stopping or halting an associated process, action, or movement.
-
C.
hasStopType
Indicates that a stop or stopping point is classified as having a particular type or category of stop.
-
D.
hasStopSpacing
Indicates that there is a specified distance or interval between consecutive stops in a route or sequence.
-
E.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
- 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_69f3498037c88190a4500f002b5540e0 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff53389a0481908b2baeb43c6294f0 |
completed | May 9, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69ff52e2b4b88190b38d160d771fe14b |
completed | May 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69ff5337e6f88190ae0418335477063c |
completed | May 9, 2026, 3:31 p.m. |
Created at: May 1, 2026, 1:41 a.m.