Triple
T32201649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peeping Tom |
E822553
|
entity |
| Predicate | impactOnCareer |
P11376
|
FINISHED |
| Object | damaged Michael Powell’s reputation at the time of release |
—
|
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 Michael Powell’s reputation at the time of release | Statement: [Peeping Tom, impactOnCareer, damaged Michael Powell’s reputation at the time of release]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnCareer Context triple: [Peeping Tom, impactOnCareer, damaged Michael Powell’s reputation at the time of release]
-
A.
careerImpact
chosen
Indicates how one entity influences or changes another entity’s professional trajectory, opportunities, or outcomes.
-
B.
workImpact
Indicates that one entity’s work has an effect or influence on another entity, situation, or outcome.
-
C.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
-
D.
impactOnSubject
Indicates the effect, influence, or consequence that one entity, event, or action has on a specified subject.
-
E.
careerTackles
Indicates the total number of tackles a player has made over the course of their entire career.
- 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_69f349093174819086e633c190a51aa8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: May 1, 2026, 12:36 a.m.