Triple
T23536693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Da Hoss |
E577617
|
entity |
| Predicate | careerEarnings |
P55500
|
FINISHED |
| Object | over US$1,900,000 |
—
|
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$1,900,000 | Statement: [Da Hoss, careerEarnings, over US$1,900,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerEarnings Context triple: [Da Hoss, careerEarnings, over US$1,900,000]
-
A.
careerEarningsApprox
chosen
Indicates an approximate total amount of money an entity has earned over the course of its career.
-
B.
payScale
Indicates the compensation level or salary range assigned to an entity, typically reflecting its relative pay or grade within a structured system.
-
C.
salary
Indicates the amount of monetary compensation an entity receives, typically on a regular basis, for work or services performed.
-
D.
income
Indicates the amount of money an entity receives, typically over a specified period, from work, investments, or other sources.
-
E.
careerSacks
Indicates the total number of times a defensive player has sacked a quarterback 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae1738bc81909a7b761ddbaa1883 |
completed | April 29, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69f118afabd88190bd88f49597d120e8 |
completed | April 28, 2026, 8:29 p.m. |
Created at: April 17, 2026, 6:10 p.m.