Triple
T27260536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LaVona Fay Golden |
E687748
|
entity |
| Predicate | characterizationInMedia |
P176804
|
FINISHED |
| Object | abusive parent |
—
|
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: abusive parent | Statement: [LaVona Fay Golden, characterizationInMedia, abusive parent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterizationInMedia Context triple: [LaVona Fay Golden, characterizationInMedia, abusive parent]
-
A.
filmCharacterDescribedAs
chosen
Indicates that a film character is described or characterized using a particular attribute, phrase, or depiction.
-
B.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
C.
posterCharacterizes
Indicates that a poster visually or textually portrays and defines the qualities, traits, or identity of something or someone.
-
D.
portraysCharacterInGenre
Indicates that an entity depicts or plays a character within works belonging to a specified genre.
-
E.
notableCharacterPortrayal
Indicates that an entity is recognized for its portrayal or depiction of a particular character, typically in a performance or narrative work.
- 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_69ef3557abc481908bf3c146f0f3356a |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f7675b12848190a3569cfda29c5b0e |
completed | May 3, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69f762f4b59481909f70074f11825bfb |
completed | May 3, 2026, 3 p.m. |
Created at: April 27, 2026, 10:52 a.m.