Triple
T22967062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khemchand Prakash |
E571073
|
entity |
| Predicate | primaryRoleInFilms |
P55759
|
FINISHED |
| Object | music director |
—
|
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: music director | Statement: [Khemchand Prakash, primaryRoleInFilms, music director]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryRoleInFilms Context triple: [Khemchand Prakash, primaryRoleInFilms, music director]
-
A.
roleInFilmEcosystem
chosen
Indicates the specific function or position an entity holds within the broader network of activities, stakeholders, and processes that make up the film ecosystem.
-
B.
relationshipRoleInFilm
Indicates that one entity has a specific relationship-based role (e.g., spouse, sibling, partner) to another entity within the context of a particular film.
-
C.
replacesInLeadRole
Indicates that one entity takes over or substitutes for another entity in the primary or leading role within a given context or production.
-
D.
primaryUsersInFilms
Indicates a relationship where certain users are the main or primary associated users for specific films.
-
E.
givenNameInFilm
Indicates that a person is referred to by a particular given (first) name within the context of a specific film.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1822f57088190addc6857063b4cca |
completed | April 29, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:48 p.m.