Triple
T8450742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Big Short |
E199791
|
entity |
| Predicate | currencyOfBoxOffice |
P32107
|
FINISHED |
| Object | USD |
—
|
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: USD | Statement: [The Big Short, currencyOfBoxOffice, USD]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currencyOfBoxOffice Context triple: [The Big Short, currencyOfBoxOffice, USD]
-
A.
currencyOfBoxOfficeGrossWorldwide
chosen
Indicates the currency in which the worldwide box office gross amount is denominated.
-
B.
countryBoxOfficeGrossUSD
Indicates the total box office revenue, in U.S. dollars, that a work earned within a specific country.
-
C.
boxOfficeGrossUSD
Indicates the total amount of money an entity earned at the box office, expressed in U.S. dollars.
-
D.
currencyCountry
Indicates that a given currency is officially used as legal tender in a particular country.
-
E.
hasBoxOfficeType
Indicates the classification of a work’s box office performance or revenue category (e.g., type or scale of its box office results).
- 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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe44815488190a912d63512e19af0 |
completed | March 31, 2026, 3:12 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:09 p.m.