Triple
T36554349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pauline Parker |
E901656
|
entity |
| Predicate | hasGenreOfNotoriety |
P88335
|
FINISHED |
| Object | true crime |
—
|
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: true crime | Statement: [Pauline Parker, hasGenreOfNotoriety, true crime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenreOfNotoriety Context triple: [Pauline Parker, hasGenreOfNotoriety, true crime]
-
A.
genreOfNotoriety
Indicates that an entity is known or famous specifically for a particular genre or category.
-
B.
hasMannerOfNotoriety
Indicates that an entity is known or recognized in a particular way, specifying the manner or type of its notoriety.
-
C.
hasNotableGenre
Indicates that an entity is significantly associated with a particular genre, such that the genre is especially characteristic or noteworthy for that entity.
-
D.
knownForGenre
chosen
Indicates that an entity is recognized or notable for working in, producing, or being associated with a particular genre.
-
E.
notableCoverGenre
Indicates that one entity is a genre in which another entity is notably covered, adapted, or reinterpreted.
- 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_69f76e634e9481908c9ba1b87ab87c26 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
Created at: May 3, 2026, 4:11 p.m.