Triple
T30933348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mindy Lahiri |
E788052
|
entity |
| Predicate | hasProfessionAsCentralTheme |
P93818
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Mindy Lahiri, hasProfessionAsCentralTheme, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionAsCentralTheme Context triple: [Mindy Lahiri, hasProfessionAsCentralTheme, true]
-
A.
hasOccupationTheme
chosen
Indicates that something (such as a work or resource) centrally involves or focuses on a particular occupation or type of work as its main theme.
-
B.
hasProfessionInNarrative
Indicates that an entity holds or is assigned a particular profession or occupational role within the context of a narrative or story.
-
C.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
D.
careerTheme
Indicates a thematic or conceptual connection between an entity and a particular career-related focus, motif, or overarching professional topic.
-
E.
hasOccupationFocus
Indicates that an entity’s occupation is primarily centered on, or specialized in, a particular field, role, or area of activity.
- 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_6a001cc0ff588190bb7c8a6fd427d02b |
completed | May 10, 2026, 5:50 a.m. |
| PD | Predicate disambiguation | batch_6a001b3ea18c8190aeda7a32b2697490 |
completed | May 10, 2026, 5:44 a.m. |
Created at: April 29, 2026, 8:52 p.m.