Triple
T7207549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | École Polytechnique massacre |
E148704
|
entity |
| Predicate | involvedSeparationByGender |
P49314
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [École Polytechnique massacre, involvedSeparationByGender, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedSeparationByGender Context triple: [École Polytechnique massacre, involvedSeparationByGender, true]
-
A.
hasGenderDivisions
chosen
Indicates that something is organized, classified, or separated into groups based on gender.
-
B.
genderIntegration
Indicates the extent to which individuals of different genders are included, mixed, or participate together within a given context or system.
-
C.
isSingleSex
Indicates that the entity involves or is restricted to only one biological sex or gender, rather than being mixed or coeducational.
-
D.
hasGenderPolicy
Indicates that an entity has adopted, implemented, or is governed by a specific policy related to gender issues or gender equality.
-
E.
admissionGender
Indicates the gender-based criteria or classification applied in the context of admission or entry decisions.
- 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_69c687e8cf188190b5f3ecffd681f04e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e969c5fc819096bc03bfba12d0cf |
completed | March 27, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69c6e757fed4819091b0a096e3befc3a |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:52 p.m.