Triple
T23624479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dixie Dwyer |
E583422
|
entity |
| Predicate | followsProfession |
P35550
|
FINISHED |
| Object | musician |
—
|
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: musician | Statement: [Dixie Dwyer, followsProfession, musician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: followsProfession Context triple: [Dixie Dwyer, followsProfession, musician]
-
A.
followsCharacterProfession
Indicates that one character’s professional role or occupation comes after or is modeled on another character’s profession.
-
B.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
C.
memberProfession
chosen
Indicates that a member or individual holds or practices a particular profession or occupation.
-
D.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
E.
refersToProfession
Indicates that one entity is being referenced specifically in terms of its profession or occupational role in relation to another 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_69e248fc8d74819091bd5baef2f36f6f |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b17be6288190a409df700c1003bd |
completed | April 29, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:46 p.m.