Triple
T15563140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gastón Gaudio |
E371046
|
entity |
| Predicate | careerPrizeMoney |
P72606
|
FINISHED |
| Object | over US$6,000,000 |
—
|
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 US$6,000,000 | Statement: [Gastón Gaudio, careerPrizeMoney, over US$6,000,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerPrizeMoney Context triple: [Gastón Gaudio, careerPrizeMoney, over US$6,000,000]
-
A.
hasPrizeMoney
chosen
Indicates that an entity awards, offers, or is associated with a specified amount of prize money.
-
B.
prizeMoneyLevel
Indicates the relative amount or tier of monetary reward associated with a prize or award.
-
C.
PgaTourMoneyListLeader
Indicates that the subject is the golfer who is currently leading the PGA Tour money list in earnings.
-
D.
equalPrizeMoneySince
Indicates that the prize money awarded has been the same for the referenced parties starting from a specific point in time.
-
E.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ddc66448190948280fb0c8d390c |
completed | April 16, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69deda7e6e748190b29ccce23298afef |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:09 a.m.