Triple
T1106573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lata Mangeshkar |
E25498
|
entity |
| Predicate | hasOccupationRole |
P2374
|
FINISHED |
| Object | playback singer in Hindi cinema |
—
|
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: playback singer in Hindi cinema | Statement: [Lata Mangeshkar, hasOccupationRole, playback singer in Hindi cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationRole Context triple: [Lata Mangeshkar, hasOccupationRole, playback singer in Hindi cinema]
-
A.
hasOrganizationalRole
Indicates that an entity holds a specific role, position, or function within an organization.
-
B.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
C.
requiredOccupationOf
Indicates that one entity specifies the occupation or job role that is required or expected for another entity (such as a position, task, or qualification).
-
D.
derivesFromOccupation
Indicates that one entity originates from, is obtained through, or is a result of another entity’s occupation or professional role.
-
E.
hasCityRole
Indicates that an entity holds or is assigned a specific role, function, or status within a particular city.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9e339f88190afc027216e95d2f7 |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b74877748190b78cd8847ee4fa7a |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:43 p.m.