Triple
T30578822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syrian Civil Defence |
E778324
|
entity |
| Predicate | hasGenderInclusion |
P109486
|
FINISHED |
| Object | includes female volunteers in some units |
—
|
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: includes female volunteers in some units | Statement: [Syrian Civil Defence, hasGenderInclusion, includes female volunteers in some units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderInclusion Context triple: [Syrian Civil Defence, hasGenderInclusion, includes female volunteers in some units]
-
A.
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.
-
B.
includesBothGenders
Indicates that the referenced group, set, or category contains members of both male and female genders.
-
C.
hasGenderRepresentation
chosen
Indicates that something includes, reflects, or portrays one or more genders within its content, structure, or composition.
-
D.
genderCategoryIncludes
Indicates that a given gender category encompasses or contains the specified gender identity or subgroup.
-
E.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
- 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_69f224a04b248190b0ca443ec86207b8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: April 29, 2026, 8:23 p.m.