Triple
T33978661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nadira Babbar |
E871213
|
entity |
| Predicate | hasChildInProfession |
P55929
|
FINISHED |
| Object | film actor |
—
|
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: film actor | Statement: [Nadira Babbar, hasChildInProfession, film actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChildInProfession Context triple: [Nadira Babbar, hasChildInProfession, film actor]
-
A.
hasChildInSameProfession
chosen
Indicates that an individual has at least one child whose profession is the same as their own.
-
B.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
C.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
D.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
E.
derivesFromOccupation
Indicates that one entity originates from, is obtained through, or is a result of another entity’s occupation or professional role.
- 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_69f3499da0188190ab1a4ff06fb06a2a |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe7eb4b8348190bb19d35766189ed4 |
completed | May 9, 2026, 12:24 a.m. |
| PD | Predicate disambiguation | batch_69fe7c35d2148190ab952e54feda1e76 |
completed | May 9, 2026, 12:13 a.m. |
Created at: May 1, 2026, 1:50 a.m.