Triple
T30933336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mindy Lahiri |
E788052
|
entity |
| Predicate | usedAsByActress |
P37984
|
FINISHED |
| Object | self-referential persona |
—
|
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: self-referential persona | Statement: [Mindy Lahiri, usedAsByActress, self-referential persona]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedAsByActress Context triple: [Mindy Lahiri, usedAsByActress, self-referential persona]
-
A.
hasAssociatedActress
Indicates that an entity is linked to an actress who is associated with it in a relevant context (e.g., participation, representation, or involvement).
-
B.
madeActressA
Indicates that one entity caused or was responsible for another entity becoming an actress.
-
C.
usedActors
Indicates that certain actors were employed or utilized in performing an action, event, or production.
-
D.
actress
Indicates that the subject is a female performer who acts in films, television, theater, or other dramatic productions.
-
E.
usedByNotablePerson
chosen
Indicates that something is or has been used by a person who is widely recognized or notable.
- 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_69f224c0b7fc819090cb89df60d23653 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
Created at: April 29, 2026, 8:52 p.m.