Triple
T32561840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1984 New York City subway shooting |
E832247
|
entity |
| Predicate | occursInVehicle |
P175488
|
FINISHED |
| Object | subway train |
—
|
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: subway train | Statement: [1984 New York City subway shooting, occursInVehicle, subway train]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occursInVehicle Context triple: [1984 New York City subway shooting, occursInVehicle, subway train]
-
A.
livesInVehicle
Indicates that an entity resides or habitually stays within a vehicle as its primary place of living or presence.
-
B.
travelsInVehicle
Indicates that an entity is moving from one place to another while being transported inside or on a vehicle.
-
C.
usedInCar
Indicates that something is utilized as a component, feature, or element within a car.
-
D.
hasVehicularUse
Indicates that something is used for, intended for, or associated with operation by vehicles or vehicular traffic.
-
E.
wasKeyVehicleIn
Indicates that a vehicle played a central or decisive role in a specified event, situation, or outcome.
- 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_69f34926b9848190ace47d2dd0a0de7c |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6d210fc80819091ed8961aa2cddfb |
completed | May 3, 2026, 4:41 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
| PDg | Predicate description generation | batch_69f6d16b79dc8190ab0d4657f2ef9a5b |
completed | May 3, 2026, 4:39 a.m. |
Created at: May 1, 2026, 1:03 a.m.