Triple
T27032586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hülya |
E680961
|
entity |
| Predicate | grammaticalGenderInTurkish |
P15730
|
FINISHED |
| Object | none (Turkish has no grammatical gender) |
—
|
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: none (Turkish has no grammatical gender) | Statement: [Hülya, grammaticalGenderInTurkish, none (Turkish has no grammatical gender)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grammaticalGenderInTurkish Context triple: [Hülya, grammaticalGenderInTurkish, none (Turkish has no grammatical gender)]
-
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.
grammaticalGenderInSpanish
Indicates that the entity has the specified grammatical gender (masculine, feminine, or neuter) in the Spanish language.
-
C.
languageNameTurkish
Indicates that the associated language’s name is given in Turkish.
-
D.
hasNoGrammaticalGender
chosen
Indicates that the referenced entity or term is not associated with any grammatical gender category in the relevant language system.
-
E.
nameInTurkish
Indicates that an entity is known or referred to by a specific name in the Turkish language.
- 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_69eeeb5566f08190813daf896fa3da04 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6223766208190a606c293dd7bb250 |
completed | May 2, 2026, 4:11 p.m. |
| PD | Predicate disambiguation | batch_69f61b3ee7b08190a0a1bc5d26b757aa |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 7:14 a.m.