Triple
T14144632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angel of the Battlefield |
E350512
|
entity |
| Predicate | hasReferentGender |
P34349
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Angel of the Battlefield, hasReferentGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReferentGender Context triple: [Angel of the Battlefield, hasReferentGender, female]
-
A.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
B.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
C.
hasTypicalGenderAssociation
chosen
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
D.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
E.
hasGrammaticalGender
Indicates that one entity assigns or possesses a specific grammatical gender in relation to another entity (such as a word, phrase, or linguistic unit).
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61214de081909a5186ff11336f97 |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b5e7a08190a16be9ad8b92b80c |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 12:53 a.m.