Triple
T6569339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City Subway 7 train |
E155392
|
entity |
| Predicate | rushHourVariantSymbol |
P26031
|
FINISHED |
| Object | <7> |
—
|
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: <7> | Statement: [New York City Subway 7 train, rushHourVariantSymbol, <7>]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rushHourVariantSymbol Context triple: [New York City Subway 7 train, rushHourVariantSymbol, <7>]
-
A.
rushHourServicePattern
chosen
Indicates that a service operates according to a specific pattern or schedule that applies only during rush-hour or peak travel times.
-
B.
peakHours
Indicates that an action, event, or condition occurs during the busiest or most heavily trafficked time period.
-
C.
EVAtime
Indicates a temporal relationship specifying when an electric vehicle (EV)–related event, action, or state occurs or is valid.
-
D.
rapidTransitSystem
Indicates a transportation relationship where people or goods are moved via a high-capacity, high-frequency public transit system designed for rapid travel over urban or regional routes.
-
E.
beltwayType
Indicates the specific classification or type of a beltway (ring road) associated with a given roadway or area.
- 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_69c688151254819080387f87deab8fa7 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acf93cb48190b54f5dd6febd34dc |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:53 p.m.