Triple
T327296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Officier de la Légion d'honneur |
E6546
|
entity |
| Predicate | regulatesPrecedence |
P1803
|
FINISHED |
| Object | French order of wear |
—
|
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: French order of wear | Statement: [Grand Officier de la Légion d'honneur, regulatesPrecedence, French order of wear]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regulatesPrecedence Context triple: [Grand Officier de la Légion d'honneur, regulatesPrecedence, French order of wear]
-
A.
confersPrecedenceIn
Indicates that one entity is granted higher priority, rank, or standing over another within a specified context or domain.
-
B.
orderPrecedence
chosen
Indicates that one entity must come before another in a defined sequence or priority order.
-
C.
precedentFor
Indicates that one situation, decision, or case serves as an authoritative example or basis for deciding or interpreting another.
-
D.
regulatesUse
Indicates that one entity controls, governs, or sets rules for how another entity may be used.
-
E.
precedentInterpreted
Indicates that one legal precedent is interpreted or understood in a particular way, often as clarified or applied in subsequent decisions or analyses.
- 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea98fa2c8190a5b44f4a26543a17 |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94aab1c8190b8654708c87eeb91 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.