Triple
T21195500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balochi cinema |
E522313
|
entity |
| Predicate | typicalProductionBudget |
P36902
|
FINISHED |
| Object | low-budget |
—
|
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: low-budget | Statement: [Balochi cinema, typicalProductionBudget, low-budget]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalProductionBudget Context triple: [Balochi cinema, typicalProductionBudget, low-budget]
-
A.
productionBudget
chosen
Indicates the amount of money allocated or spent to produce a work, such as a film, show, or similar production.
-
B.
productionValue
Indicates the quantitative amount or worth of output generated in a production process or activity.
-
C.
wasOneOfMostExpensiveFilmsOf
Indicates that a film ranked among the most expensive films produced in the specified context (such as a given time period, region, or category).
-
D.
boxOfficeGrossUSD
Indicates the total amount of money an entity earned at the box office, expressed in U.S. dollars.
-
E.
formerHighestGrossingFilm
Indicates that a film once held, but no longer holds, the record for the highest box-office gross.
- 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7333aa0fc81909b17eb6a26f389ec |
completed | April 21, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69e5f6094e3c81909ee9699e00d371f7 |
completed | April 20, 2026, 9:46 a.m. |
Created at: April 16, 2026, 3:08 p.m.