Triple
T1765482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Men of the Sahara |
E38751
|
entity |
| Predicate | genderRole |
P25470
|
FINISHED |
| Object | women often manage tents and property |
—
|
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: women often manage tents and property | Statement: [Blue Men of the Sahara, genderRole, women often manage tents and property]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderRole Context triple: [Blue Men of the Sahara, genderRole, women often manage tents and property]
-
A.
genderRule
Indicates a rule or constraint that determines how gender-related properties or classifications should be assigned or interpreted in a given context.
-
B.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
C.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
D.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
E.
genderDivision
chosen
Indicates a relationship where roles, responsibilities, or categories are separated or distinguished based on 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_69a8862d562481908d7025a1c1f67c0d |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab39fc2c448190bfaf1ee8d474632a |
completed | March 6, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69aa61cbb1288190a7ba38b61905f578 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.