Triple
T29542220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonina |
E749533
|
entity |
| Predicate | hasProfessionalLifeDepicted |
P7041
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Antonina, hasProfessionalLifeDepicted, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalLifeDepicted Context triple: [Antonina, hasProfessionalLifeDepicted, yes]
-
A.
hasOccupationInReality
Indicates that an entity holds or performs a specific occupation in the real world, as opposed to fictional or hypothetical contexts.
-
B.
hasProfessionalCareer
Indicates that an entity engages in or has engaged in a recognized professional occupation or career over a period of time.
-
C.
portraysProfession
chosen
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
D.
hasAdultLifeIn
Indicates that an entity spends or experiences its adult stage of life within a specified location or environment.
-
E.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
- 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_69f0bd48691081908cecad39bac591e0 |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69ff80d9a1d88190a95b1488acd6e2e5 |
completed | May 9, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_69ff802ae2dc819093a3cda42b63dcbd |
completed | May 9, 2026, 6:42 p.m. |
Created at: April 28, 2026, 5:03 p.m.