Triple
T31978249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Will Traynor |
E816504
|
entity |
| Predicate | preAccidentLifestyle |
P173075
|
FINISHED |
| Object | high-powered corporate career |
—
|
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: high-powered corporate career | Statement: [Will Traynor, preAccidentLifestyle, high-powered corporate career]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: preAccidentLifestyle Context triple: [Will Traynor, preAccidentLifestyle, high-powered corporate career]
-
A.
safetyRecordBeforeAccident
Indicates that an entity’s documented history of safety performance or incidents existed prior to a specific accident or adverse event.
-
B.
goalBeforeAccident
Indicates that one event or state was the intended goal or target prior to the occurrence of an accident.
-
C.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
D.
hasModeOfTransportInAccident
Indicates that a specific mode of transport was involved in an accident associated with the given entity.
-
E.
accidentOccurredDuring
Indicates that an accident took place within the time span or context of a specified event, activity, or condition.
- 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_69f348f6a3008190bfb59ca695fd68e2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b34672f08190bd219f937ce6b1dd |
completed | May 3, 2026, 2:30 a.m. |
| PD | Predicate disambiguation | batch_69f6b151ad008190836c1bcdec503ce2 |
completed | May 3, 2026, 2:22 a.m. |
| PDg | Predicate description generation | batch_69f6b21da77081908c5c015c4606d344 |
completed | May 3, 2026, 2:25 a.m. |
Created at: May 1, 2026, 12:11 a.m.