Triple
T14143496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Master and Brothers |
E350483
|
entity |
| Predicate | hasGenderedHistoricalTerm |
P112983
|
FINISHED |
| Object | Brothers |
—
|
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: Brothers | Statement: [Master and Brothers, hasGenderedHistoricalTerm, Brothers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderedHistoricalTerm Context triple: [Master and Brothers, hasGenderedHistoricalTerm, Brothers]
-
A.
hasGenderHistory
Indicates that an entity has undergone or experienced a change or transition in gender over time.
-
B.
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.
-
C.
usesGenderAccurateLanguage
Indicates that the language employed in the context correctly reflects and respects the gender identities of the entities referenced.
-
D.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
E.
formerGenderAdmission
Indicates that an institution previously admitted a particular gender but no longer does so.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69de239a02e881909b0e2679487e4ab2 |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 10, 2026, 12:52 a.m.