Triple
T19751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevalier de la Légion d'honneur |
E393
|
entity |
| Predicate | positionInOrder |
P1109
|
FINISHED |
| Object | lowest rank |
—
|
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: lowest rank | Statement: [Chevalier de la Légion d'honneur, positionInOrder, lowest rank]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionInOrder Context triple: [Chevalier de la Légion d'honneur, positionInOrder, lowest rank]
-
A.
namePosition
Indicates the positional or ordering relationship of a name within a sequence or structured context (e.g., first, last, or specific index).
-
B.
orderInOffice
Indicates that one entity holds a specific sequential position or rank within a defined term or period of holding an office or official role.
-
C.
positionHeld
Indicates that an entity occupies or has occupied a specific role, job, office, or position within an organization or context.
-
D.
hasOrder
chosen
Indicates that one entity possesses, is associated with, or is characterized by a specific order, sequence, or arrangement relative to others.
-
E.
numberOfPositions
Indicates the total count of distinct positions or roles associated with a given 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.