Triple
T2469746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IRT A Division subway cars |
E55340
|
entity |
| Predicate | widthPerCar |
P31314
|
FINISHED |
| Object | approximately 8.6 feet |
—
|
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: approximately 8.6 feet | Statement: [IRT A Division subway cars, widthPerCar, approximately 8.6 feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: widthPerCar Context triple: [IRT A Division subway cars, widthPerCar, approximately 8.6 feet]
-
A.
usesCarWidth
Indicates that one entity determines, measures, or constrains something based on the width of a car.
-
B.
carWidth
chosen
Indicates the measurement of how wide a car is across its lateral (side-to-side) dimension.
-
C.
rowsPerCar
Indicates the number of rows associated with or allocated to each individual car.
-
D.
hasCarLength
Indicates that an entity is associated with a specific measurement representing the length of a car.
-
E.
numberOfCarsPerUnit
Indicates the quantity of cars associated with each single unit of a specified measure (such as time, distance, or entity).
- 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2bc7b5481908b3664495e99f1a4 |
completed | March 7, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69abd0b3ea308190a6d8499c2a542c50 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.