Triple
T27546719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Payton Chester |
E695381
|
entity |
| Predicate | numberOfPeopleInSameAccident |
P154584
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Payton Chester, numberOfPeopleInSameAccident, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleInSameAccident Context triple: [Payton Chester, numberOfPeopleInSameAccident, 9]
-
A.
numberOfVictimsInSameEvent
chosen
Indicates the count of distinct victims involved in the same specific event or incident.
-
B.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
C.
additionalDeathsRelatedToAccident
Indicates that there were extra fatalities occurring as a consequence of the accident beyond any initially recorded or primary deaths.
-
D.
resultOfAccident
Indicates that something exists or occurs as a consequence or outcome of an accident.
-
E.
involvedInAccident
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
- 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_69ef5386c3e08190bfe33aa326e1f72b |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69fee335cb08819097e3a0e09d5ebf49 |
completed | May 9, 2026, 7:33 a.m. |
| PD | Predicate disambiguation | batch_69fee2c74fd88190acfc045ab07b7f6b |
completed | May 9, 2026, 7:31 a.m. |
Created at: April 27, 2026, 1:33 p.m.