Triple
T67262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Légion d'honneur |
E1340
|
entity |
| Predicate | awardingFrequency |
P2205
|
FINISHED |
| Object | continuous since establishment |
—
|
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: continuous since establishment | Statement: [Légion d'honneur, awardingFrequency, continuous since establishment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardingFrequency Context triple: [Légion d'honneur, awardingFrequency, continuous since establishment]
-
A.
awardedFrequency
chosen
Indicates how often an award or recognition is given within a specified time period.
-
B.
awardedDuring
Indicates that an award or honor was given to an entity within a specified time period or event.
-
C.
lastAwarded
Indicates the most recent time or instance at which an entity received a particular award.
-
D.
hasAwarded
Indicates that one entity has given or conferred an award to another entity.
-
E.
canBeAwardedMultipleTimes
Indicates that the associated award, honor, or recognition may be granted to the same recipient on more than one occasion.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a2509b5a088190bb9d2b650aeb8bca |
completed | Feb. 28, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69a24ea749788190bc17865171ff909a |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.