Triple
T36194624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Division (New York City Subway) |
E1047087
|
entity |
| Predicate | usesCarProfile |
P173214
|
FINISHED |
| Object | narrow IRT car profile |
—
|
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: narrow IRT car profile | Statement: [A Division (New York City Subway), usesCarProfile, narrow IRT car profile]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCarProfile Context triple: [A Division (New York City Subway), usesCarProfile, narrow IRT car profile]
-
A.
hasCarClass
Indicates that one entity is associated with, or categorized under, a particular class or type of car.
-
B.
usedByVehicleType
chosen
Indicates that something (such as a resource, component, or facility) is utilized or operated by a specific type or category of vehicle.
-
C.
carBodyProfile
Indicates the overall shape, contours, and structural outline that define a car’s external body form.
-
D.
vehicleUsed
Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
-
E.
usesVehicleVariant
Indicates that one entity performs an action or function by employing a specific variant or version of a vehicle.
- 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_69f76e3d4fbc81908c159c7beeb4ce00 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fe031bc6208190860099aef72d8dcb |
completed | May 8, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69fe014c8b388190b5d4e0cb95ee2be5 |
completed | May 8, 2026, 3:29 p.m. |
Created at: May 3, 2026, 4:08 p.m.