Triple
T32260056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Wambaugh |
E824125
|
entity |
| Predicate | basedOnExperienceAs |
P88172
|
FINISHED |
| Object | LAPD detective |
—
|
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: LAPD detective | Statement: [Joseph Wambaugh, basedOnExperienceAs, LAPD detective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnExperienceAs Context triple: [Joseph Wambaugh, basedOnExperienceAs, LAPD detective]
-
A.
basedOnExperience
Indicates that something is determined, chosen, or formed according to prior experience or experiential knowledge.
-
B.
basedOnExpertiseOf
Indicates that something is determined, derived, or justified using the knowledge, skills, or judgment of a particular expert or group of experts.
-
C.
basedOnCareerOf
chosen
Indicates that something (such as a work, character, or storyline) is derived from, inspired by, or modeled on the career or professional life of a particular person.
-
D.
usedExperienceIn
Indicates that an entity applied or leveraged a particular experience or expertise in performing an action or achieving a result.
-
E.
hasExperienceOf
Indicates that one entity has undergone, encountered, or lived through a particular event, situation, or activity associated with another entity.
- 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_69f3490db0748190bfef6e50c95d39d3 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe6739d4dc8190ae7505c089bbac29 |
completed | May 8, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69fe6541dffc81909c66a61ba69f38fc |
completed | May 8, 2026, 10:35 p.m. |
Created at: May 1, 2026, 12:41 a.m.