Triple
T9580722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2009 Fort Totten crash |
E231160
|
entity |
| Predicate | victimCountCategory |
P12810
|
FINISHED |
| Object | mass-casualty incident |
—
|
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: mass-casualty incident | Statement: [2009 Fort Totten crash, victimCountCategory, mass-casualty incident]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: victimCountCategory Context triple: [2009 Fort Totten crash, victimCountCategory, mass-casualty incident]
-
A.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
-
B.
fatalitiesCategory
chosen
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
C.
numberOfVictimsClaimed
Indicates the reported count of victims associated with a particular event, incident, or action.
-
D.
mainVictims
Indicates that the related entities are the primary or principal targets harmed or affected by an action, event, or perpetrator.
-
E.
childrenKilledBy
Indicates that the children of a given entity were killed by another specified entity or agent.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99cbe79081909947b4d1389eb015 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:05 p.m.