Triple
T32430650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Simmons |
E828712
|
entity |
| Predicate | isGenderedName |
P104114
|
FINISHED |
| Object | typically 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: typically masculine | Statement: [Michael Simmons, isGenderedName, typically masculine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isGenderedName Context triple: [Michael Simmons, isGenderedName, typically masculine]
-
A.
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.
-
B.
genderOfName
chosen
Indicates the gender typically associated with a given name.
-
C.
genderOfEponym
Indicates the gender of the person after whom something (such as a place, object, or concept) is named.
-
D.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
E.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
- 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_69f3491bf298819097b610f772d54a6d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c2ae4f388190b97bfca23ce5ddcd |
completed | May 3, 2026, 3:36 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6eb32c8190bf405b2011fa48f7 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:55 a.m.