Triple
T10037857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enterprise |
E205216
|
entity |
| Predicate | ferryFlightIncidents |
P92059
|
FINISHED |
| Object | damaged by Hurricane Sandy while on display in New York |
—
|
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: damaged by Hurricane Sandy while on display in New York | Statement: [Enterprise, ferryFlightIncidents, damaged by Hurricane Sandy while on display in New York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ferryFlightIncidents Context triple: [Enterprise, ferryFlightIncidents, damaged by Hurricane Sandy while on display in New York]
-
A.
missionAccident
Indicates that an accident or unintended harmful event occurred during the course of a mission or operation.
-
B.
flightNumberInAccident
Indicates that a specific flight number is associated with an accident event.
-
C.
flightOverWater
Indicates that a flight’s route passes over a body of water.
-
D.
aircraftAccidentYear
Indicates the calendar year in which an aircraft accident occurred.
-
E.
aircraftImpact
Indicates that an aircraft collides with or crashes into a target or surface, causing an impact event.
- 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_69ca834f70e88190b2d74828b7767ec1 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcede428c8190ae115dc3425f9b0e |
completed | April 2, 2026, 2:05 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8638508190b22acc65500ec7d6 |
completed | April 1, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69cd4fed19d481909d2c7ff1114664b6 |
completed | April 1, 2026, 5:03 p.m. |
Created at: March 30, 2026, 8:55 p.m.