Triple
T24395605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 25th Rifle Division |
E615018
|
entity |
| Predicate | hasGenderIntegration |
P62384
|
FINISHED |
| Object | included female combatants |
—
|
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: included female combatants | Statement: [25th Rifle Division, hasGenderIntegration, included female combatants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderIntegration Context triple: [25th Rifle Division, hasGenderIntegration, included female combatants]
-
A.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
B.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
C.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
D.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
E.
genderIntegration
chosen
Indicates the extent to which individuals of different genders are included, mixed, or participate together within a given context or system.
- 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_69e2d7e509b88190a53155d4f3de45ce |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f294d69b5c81908cf6143374934f5c |
completed | April 29, 2026, 11:31 p.m. |
| PD | Predicate disambiguation | batch_69f287c4a2b48190b80fb7a3c0e9b018 |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 2:04 a.m.