Triple
T19776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevalier de la Légion d'honneur |
E393
|
entity |
| Predicate | hasPostnominalLetters |
P1804
|
FINISHED |
| Object | no official postnominals in France |
—
|
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: no official postnominals in France | Statement: [Chevalier de la Légion d'honneur, hasPostnominalLetters, no official postnominals in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPostnominalLetters Context triple: [Chevalier de la Légion d'honneur, hasPostnominalLetters, no official postnominals in France]
-
A.
honorificSuffix
Indicates that one entity is a respectful or formal suffix appended to another entity’s name or title.
-
B.
honorificPrefix
Indicates the formal title or respectful prefix (e.g., "Dr.", "Mr.", "Prof.") used before a person's name to denote status, role, or honor.
-
C.
hasHonorificPrefix
Indicates that one entity is used as an honorific title or prefix attached to another entity’s name.
-
D.
namedAfter
Indicates that one entity has been given its name in honor of, or derived from, another entity.
-
E.
namePosition
Indicates the positional or ordering relationship of a name within a sequence or structured context (e.g., first, last, or specific index).
- 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.