Triple
T2241365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Per Anger Prize |
E49401
|
entity |
| Predicate | namedAfterOccupationOfEponym |
P365
|
FINISHED |
| Object | Swedish diplomat |
—
|
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: Swedish diplomat | Statement: [Per Anger Prize, namedAfterOccupationOfEponym, Swedish diplomat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namedAfterOccupationOfEponym Context triple: [Per Anger Prize, namedAfterOccupationOfEponym, Swedish diplomat]
-
A.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
B.
namesakeOccupation
chosen
Indicates that one entity’s occupation is the same as, or derived from, the occupation associated with the other entity’s namesake.
-
C.
honorificEponym
Indicates that one entity serves as an honorific namesake for another, typically recognizing or commemorating the person or entity in whose honor something is named.
-
D.
hasAwardNamedAfter
Indicates that an entity has an award that is named in honor of another entity.
-
E.
notableOccupationContext
Indicates that the referenced occupation is notable or significant specifically within the given contextual framework or domain.
- 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_69a88aa979788190ad6500f1d8eee2fc |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc0be7fb4819081a5f9c46b616bdb |
completed | March 7, 2026, 6:07 a.m. |
| PD | Predicate disambiguation | batch_69abbdafc07881909101266a33ae7031 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:47 p.m.