Triple
T4437994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arrogate |
E95697
|
entity |
| Predicate | careerEarningsRank |
P25601
|
FINISHED |
| Object | one of the highest-earning Thoroughbreds in history |
—
|
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: one of the highest-earning Thoroughbreds in history | Statement: [Arrogate, careerEarningsRank, one of the highest-earning Thoroughbreds in history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerEarningsRank Context triple: [Arrogate, careerEarningsRank, one of the highest-earning Thoroughbreds in history]
-
A.
wealthRanking
chosen
Indicates the relative ordering of entities based on their level of wealth or financial resources.
-
B.
careerSacks
Indicates the total number of times a defensive player has sacked a quarterback over the course of their entire career.
-
C.
salary
Indicates the amount of monetary compensation an entity receives, typically on a regular basis, for work or services performed.
-
D.
peakEmployment
Indicates that an entity has reached its highest level of employment or workforce size during a specified period.
-
E.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3558c75948190875a5fb10eac0597 |
completed | March 13, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:31 p.m.