Triple
T19453249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Prodigy (2019 film) |
E486668
|
entity |
| Predicate | boxOfficeGrossOtherTerritories |
P1960
|
FINISHED |
| Object | approximately $6.1 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: approximately $6.1 million | Statement: [The Prodigy (2019 film), boxOfficeGrossOtherTerritories, approximately $6.1 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: boxOfficeGrossOtherTerritories Context triple: [The Prodigy (2019 film), boxOfficeGrossOtherTerritories, approximately $6.1 million]
-
A.
countryBoxOfficeGrossUSD
Indicates the total box office revenue, in U.S. dollars, that a work earned within a specific country.
-
B.
boxOfficeGrossUSD
chosen
Indicates the total amount of money an entity earned at the box office, expressed in U.S. dollars.
-
C.
currencyOfBoxOfficeGrossWorldwide
Indicates the currency in which the worldwide box office gross amount is denominated.
-
D.
boxOfficeGrossJapan
Indicates the amount of money a film or other media work has earned at the box office specifically in Japan.
-
E.
boxOfficeOpeningWeekendUS
Indicates the amount of money a film earns at the box office in the United States during its opening weekend.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6339407a08190a3e0213bfbb4df3d |
completed | April 20, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7499a4819082bec0be8afba35c |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.