Triple
T23509418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Rum |
E572374
|
entity |
| Predicate | careerSeconds |
P152647
|
FINISHED |
| Object | 15 |
—
|
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: 15 | Statement: [Red Rum, careerSeconds, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerSeconds Context triple: [Red Rum, careerSeconds, 15]
-
A.
careerSeasons
Indicates the number or set of seasons during which an entity actively participated in a particular career or professional role.
-
B.
timeInCareer
Indicates the point or duration within an entity’s professional or occupational trajectory at which a related event, status, or condition occurs.
-
C.
careerRuns
Indicates the total number of runs a player has scored over the entire duration of their professional career.
-
D.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
careerPoints
Indicates the total number of points an individual has accumulated over the course of their entire career in a given activity or domain.
- F. None of above. chosen
Provenance (4 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_69e245b5e4208190bac8a6509867e394 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a902c0788190840d7df1b5450b4d |
completed | April 29, 2026, 6:45 a.m. |
| PD | Predicate disambiguation | batch_69f0621165c08190a0b27b1319733959 |
completed | April 28, 2026, 7:30 a.m. |
| PDg | Predicate description generation | batch_69f0bd4a0e408190ad8916faf23562d9 |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 17, 2026, 6:07 p.m.