Triple
T23536845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mucho Macho Man |
E577620
|
entity |
| Predicate | careerLongevity |
P18004
|
FINISHED |
| Object | raced at top level through age 6 |
—
|
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: raced at top level through age 6 | Statement: [Mucho Macho Man, careerLongevity, raced at top level through age 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerLongevity Context triple: [Mucho Macho Man, careerLongevity, raced at top level through age 6]
-
A.
careerSeasons
Indicates the number or set of seasons during which an entity actively participated in a particular career or professional role.
-
B.
careerRuns
Indicates the total number of runs a player has scored over the entire duration of their professional career.
-
C.
careerSpanTeam
Indicates the team for which an individual’s entire career span (or a defined portion of it) is being measured or associated.
-
D.
activeYearsInCareer
chosen
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
timeInCareer
Indicates the point or duration within an entity’s professional or occupational trajectory at which a related event, status, or condition occurs.
- 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.