Triple
T13387897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeni Turan |
E319491
|
entity |
| Predicate | hasFemaleCharacters |
P99057
|
FINISHED |
| Object | politically active women |
—
|
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: politically active women | Statement: [Yeni Turan, hasFemaleCharacters, politically active women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleCharacters Context triple: [Yeni Turan, hasFemaleCharacters, politically active women]
-
A.
hasFemaleCharacter
chosen
Indicates that an entity includes or features at least one female character.
-
B.
hasStrongFemaleCharacters
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
-
C.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
-
D.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
E.
hasFemaleSpeaker
Indicates that the associated content, event, or communication is spoken or narrated by a female individual.
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dba0d3a40081909ba49556130ad0e7 |
completed | April 12, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69d9a03189908190a784a2755f8d81e1 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:34 p.m.