Triple
T19782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevalier de la Légion d'honneur |
E393
|
entity |
| Predicate | hasGenderedTitle |
P1805
|
FINISHED |
| Object | Chevalier (masculine) |
—
|
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: Chevalier (masculine) | Statement: [Chevalier de la Légion d'honneur, hasGenderedTitle, Chevalier (masculine)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderedTitle Context triple: [Chevalier de la Légion d'honneur, hasGenderedTitle, Chevalier (masculine)]
-
A.
hasGenderPolicy
Indicates that an entity has adopted, implemented, or is governed by a specific policy related to gender issues or gender equality.
-
B.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
C.
title
Indicates that one entity serves as the formal name or designation of another entity.
-
D.
sexualDimorphism
Indicates differences in physical characteristics between males and females of a species that are systematically associated with their sex.
-
E.
givenName
Indicates the personal first name assigned to an individual.
- 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_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a24703cb988190ad2bc181d27829e4 |
completed | Feb. 28, 2026, 1:38 a.m. |
| PD | Predicate disambiguation | batch_69a24650f1f0819081e638fafd18d687 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a24702d4988190a54a4e578b7c919e |
completed | Feb. 28, 2026, 1:38 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.