Triple
T654736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Officer of the Order of Arts and Letters of France |
E11621
|
entity |
| Predicate | hasGradeWithinOrder |
P17983
|
FINISHED |
| Object | Officer |
—
|
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: Officer | Statement: [Officer of the Order of Arts and Letters of France, hasGradeWithinOrder, Officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGradeWithinOrder Context triple: [Officer of the Order of Arts and Letters of France, hasGradeWithinOrder, Officer]
-
A.
isGradeWithin
Indicates that a given grade value falls within a specified acceptable or defined grade range.
-
B.
hasGradeCount
Indicates a relationship where an entity is associated with the number of grades it has or has received.
-
C.
hasOrder
Indicates that one entity possesses, is associated with, or is characterized by a specific order, sequence, or arrangement relative to others.
-
D.
orderGradeLevel
Indicates the relative sequencing or ranking of grade levels, specifying which grade comes before or after another.
-
E.
orderHasThreeGrades
Indicates that an order is associated with exactly three distinct grades or levels.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4bb5b881908a18b5ec1c94e0cf |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d121cec81909986c91291bb4ca8 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49ee356c0819085e2e82831cf1360 |
completed | March 1, 2026, 8:17 p.m. |
Created at: March 1, 2026, 7:36 p.m.