Triple
T1700262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R179 subway cars |
E36751
|
entity |
| Predicate | carWidth |
P31314
|
FINISHED |
| Object | approximately 9.8 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 9.8 feet | Statement: [R179 subway cars, carWidth, approximately 9.8 feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carWidth Context triple: [R179 subway cars, carWidth, approximately 9.8 feet]
-
A.
usesCarWidth
Indicates that one entity determines, measures, or constrains something based on the width of a car.
-
B.
hasCarLength
Indicates that an entity is associated with a specific measurement representing the length of a car.
-
C.
wheelbase
Indicates the distance between the centers of the front and rear wheels of a vehicle.
-
D.
vehicleStandard
Indicates that something complies with, or is defined according to, a specified vehicle-related standard or regulatory specification.
-
E.
driverDiameter
Indicates the size of the circular cross-section of a driver component, typically measured as the distance across its widest point.
- 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_69a886163dec8190859c514232a37a05 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf169da888190b3aa334752f1952b |
completed | March 6, 2026, 3:23 p.m. |
| PD | Predicate disambiguation | batch_69aa61b8ce348190b46154af0b041ff0 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69aaf16865488190a76577b36760dc7a |
completed | March 6, 2026, 3:23 p.m. |
Created at: March 4, 2026, 7:30 p.m.