Triple
T817214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Provoked Wife |
E17674
|
entity |
| Predicate | hasStrongFemaleCharacters |
P19972
|
FINISHED |
| Object | true |
—
|
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 | Statement: [The Provoked Wife, hasStrongFemaleCharacters, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStrongFemaleCharacters Context triple: [The Provoked Wife, hasStrongFemaleCharacters, true]
-
A.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
B.
hasGenderFocus
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
-
C.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
D.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
E.
hasGenderedTitle
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab621d2c819083f10bff4f66c482 |
completed | March 1, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69a4aa756920819080ae82948974c876 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4781c88190ae36906251347cdc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.