Triple
T5665780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Frisian |
E124852
|
entity |
| Predicate | grammaticalGenders |
P3087
|
FINISHED |
| Object | masculine |
—
|
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: masculine | Statement: [Old Frisian, grammaticalGenders, masculine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grammaticalGenders Context triple: [Old Frisian, grammaticalGenders, masculine]
-
A.
hasGrammaticalGender
chosen
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.
hasNoGrammaticalGender
Indicates that the referenced entity or term is not associated with any grammatical gender category in the relevant language system.
-
C.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
D.
hasFeminineFormInSomeLanguages
Indicates that the referenced entity has a distinct feminine grammatical or lexical form in at least one language.
-
E.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic 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_69c00828906881908966f270b8f130cf |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0236d3f94819095111c41a323612d |
completed | March 22, 2026, 5:14 p.m. |
| PD | Predicate disambiguation | batch_69c021ba4ec481909db8cdbf0e907dd6 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:43 p.m.