Triple
T25490949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nandamuri family |
E638834
|
entity |
| Predicate | notableProfession |
P22
|
FINISHED |
| Object | 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: actor | Statement: [Nandamuri family, notableProfession, actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableProfession Context triple: [Nandamuri family, notableProfession, actor]
-
A.
notableOccupationContext
Indicates that the referenced occupation is notable or significant specifically within the given contextual framework or domain.
-
B.
notableHolderOccupation
Indicates that a person notably associated with an entity (e.g., an award, office, or title) held a particular occupation or professional role.
-
C.
notableProfessor
Indicates that a person holds or has held a professorship that is distinguished, prominent, or otherwise recognized as notable.
-
D.
actorNotableOccupation
Indicates that a person (typically an actor) is associated with a particular occupation or professional role for which they are especially well known.
-
E.
notableFor
chosen
Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or 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_69e75dbbd2a88190b70e1e645de14b9a |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69fd02680d948190a3463fb119ba8556 |
completed | May 7, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69fcf89c69b4819082bbc564bd15137d |
completed | May 7, 2026, 8:39 p.m. |
Created at: April 21, 2026, 2:38 p.m.