Triple
T19682935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ashura statue |
E472637
|
entity |
| Predicate | subjectHasGenderExpression |
P55521
|
FINISHED |
| Object | androgynous |
—
|
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: androgynous | Statement: [Ashura statue, subjectHasGenderExpression, androgynous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectHasGenderExpression Context triple: [Ashura statue, subjectHasGenderExpression, androgynous]
-
A.
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).
-
B.
hasGenderInterpretation
chosen
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
C.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
D.
hasGenderInText
Indicates that a specified gender is explicitly mentioned or assigned to an entity within a given text.
-
E.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641c126b88190820281058a1e7793 |
completed | April 20, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69e53039ea808190a9106a53f564ab92 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:45 p.m.