Triple
T21109834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teri Yaad |
E520141
|
entity |
| Predicate | hasOriginalCountryFilmIndustry |
P101250
|
FINISHED |
| Object | Pakistani film industry |
—
|
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: Pakistani film industry | Statement: [Teri Yaad, hasOriginalCountryFilmIndustry, Pakistani film industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOriginalCountryFilmIndustry Context triple: [Teri Yaad, hasOriginalCountryFilmIndustry, Pakistani film industry]
-
A.
originatesInFilmIndustry
chosen
Indicates that something has its source, development, or primary origin within the film industry.
-
B.
originallyProducedIn
Indicates that something was first created, manufactured, or brought into existence in a particular place or location.
-
C.
hasCinematographerNationality
Indicates that a cinematographer is associated with a specific nationality.
-
D.
filmCountryOfOrigin
Indicates the country where a film was originally produced or created.
-
E.
createdByCountryOfOrigin
Indicates that something was created by the country from which it originally comes or is associated as its origin.
- 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_69e0b509a318819092fbbcb21d1fe603 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7210110a48190a6359b6732f6293d |
completed | April 21, 2026, 7:02 a.m. |
| PD | Predicate disambiguation | batch_69e5dbff56848190a03b350a9305c612 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:54 p.m.