Triple
T19425562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AAR-02-01 |
E485970
|
entity |
| Predicate | numberOfFatalitiesInvestigatedAccident |
P135834
|
FINISHED |
| Object | 88 |
—
|
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: 88 | Statement: [AAR-02-01, numberOfFatalitiesInvestigatedAccident, 88]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFatalitiesInvestigatedAccident Context triple: [AAR-02-01, numberOfFatalitiesInvestigatedAccident, 88]
-
A.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
B.
causedFatalities
Indicates that the referenced event or action directly resulted in one or more deaths.
-
C.
numberOfVictimsConfirmed
Indicates the confirmed count of victims associated with an event, incident, or situation.
-
D.
numberOfVictimsInjured
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
E.
constructionAccidentFatalities
Indicates that a construction-related accident resulted in one or more fatalities.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63218772c8190a48b6cb01bd12b73 |
completed | April 20, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69e4fd68b1f881908d273de1fee81a75 |
completed | April 19, 2026, 4:06 p.m. |
| PDg | Predicate description generation | batch_69e5004c23308190a087b7941a90725f |
completed | April 19, 2026, 4:18 p.m. |
Created at: April 10, 2026, 1:37 p.m.