Triple
T14956853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brad Pitt as Metro Man |
E372952
|
entity |
| Predicate | filmBoxOfficeGrossWorldwideUSD |
P1960
|
FINISHED |
| Object | over 320 million |
—
|
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: over 320 million | Statement: [Brad Pitt as Metro Man, filmBoxOfficeGrossWorldwideUSD, over 320 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmBoxOfficeGrossWorldwideUSD Context triple: [Brad Pitt as Metro Man, filmBoxOfficeGrossWorldwideUSD, over 320 million]
-
A.
boxOfficeGrossUSD
chosen
Indicates the total amount of money an entity earned at the box office, expressed in U.S. dollars.
-
B.
currencyOfBoxOfficeGrossWorldwide
Indicates the currency in which the worldwide box office gross amount is denominated.
-
C.
countryBoxOfficeGrossUSD
Indicates the total box office revenue, in U.S. dollars, that a work earned within a specific country.
-
D.
formerHighestGrossingFilm
Indicates that a film once held, but no longer holds, the record for the highest box-office gross.
-
E.
hasBoxOffice
Indicates that an entity (typically a film or performance) has a specific box office revenue amount or record associated with it.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cc73848190ac181782b20dc838 |
completed | April 15, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69de9a5d995881909e33658f5aea5582 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:40 a.m.