Triple
T33199758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amar Varma |
E849865
|
entity |
| Predicate | filmBoxOfficeRegion |
P171837
|
FINISHED |
| Object | India |
—
|
NE NERFINISHED |
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: India | Statement: [Amar Varma, filmBoxOfficeRegion, India]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmBoxOfficeRegion Context triple: [Amar Varma, filmBoxOfficeRegion, India]
-
A.
boxOfficeTerritoryLabel
Indicates the specific geographic territory or market associated with a box office performance or revenue figure.
-
B.
countryBoxOfficeGrossUSD
Indicates the total box office revenue, in U.S. dollars, that a work earned within a specific country.
-
C.
filmRegion
chosen
Indicates the geographic region or market in which a film is released, distributed, or primarily associated.
-
D.
boxOfficeWorldwideINR
Indicates the total worldwide box office revenue of a work, expressed in Indian Rupees (INR).
-
E.
boxOfficeInternationalUSD
Indicates the amount of money a work earned in international (non-domestic) box offices, measured in U.S. dollars.
- 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_69f3495efedc8190843a5728089544b9 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6dd3cc0648190a275812d6711275a |
completed | May 3, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69f6d82eaee081908f06a71546315aea |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 1:29 a.m.