Triple
T19777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevalier de la Légion d'honneur |
E393
|
entity |
| Predicate | typicalRecipients |
P485
|
FINISHED |
| Object | senior military officers |
—
|
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: senior military officers | Statement: [Chevalier de la Légion d'honneur, typicalRecipients, senior military officers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRecipients Context triple: [Chevalier de la Légion d'honneur, typicalRecipients, senior military officers]
-
A.
scopeOfRecipients
chosen
Indicates the range or group of recipients to whom something (such as information, communication, or benefits) is directed or applicable.
-
B.
notableRecipient
Indicates that an entity has received a notable award, honor, or recognition from another entity.
-
C.
coRecipientWith
Indicates that two or more entities receive the same item, benefit, or communication together as joint recipients.
-
D.
hasTypeOfRecipient
Indicates that an entity is associated with a specific category or kind of recipient it is intended for or directed to.
-
E.
addresses
Indicates that one entity directs speech, communication, or written correspondence specifically toward another entity.
- 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_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. |
Created at: Feb. 28, 2026, 1:14 a.m.