Triple
T12644233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomery Brewster |
E301978
|
entity |
| Predicate | inheritanceAmountInFilm |
P23690
|
FINISHED |
| Object | 30 million dollars |
—
|
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: 30 million dollars | Statement: [Montgomery Brewster, inheritanceAmountInFilm, 30 million dollars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inheritanceAmountInFilm Context triple: [Montgomery Brewster, inheritanceAmountInFilm, 30 million dollars]
-
A.
netWorthAtDeath
Indicates the total value of a person’s assets minus liabilities at the time of their death.
-
B.
inheritsWealth
chosen
Indicates that one entity receives wealth or assets passed down from another, typically after the latter’s death.
-
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.
basedOnInFilm
Indicates that a film is derived from, adapted from, or otherwise uses as its source material another work, event, or concept.
-
E.
firstAwardedForFilm
Indicates the film for which an entity (such as a person or award) was first given or received an award.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960b47130819097e1162ed4fc993a |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:17 p.m.