Triple
T25279514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bark at the Moon |
E633788
|
entity |
| Predicate | careerSignificanceFor |
P158439
|
FINISHED |
| Object | Ozzy Osbourne |
—
|
NE NERFINISHED |
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: Ozzy Osbourne | Statement: [Bark at the Moon, careerSignificanceFor, Ozzy Osbourne]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerSignificanceFor Context triple: [Bark at the Moon, careerSignificanceFor, Ozzy Osbourne]
-
A.
careerAssists
Indicates the total number of assists a player has recorded over the entire span of their professional or competitive career.
-
B.
careerField
Indicates the professional domain or occupational area in which an entity works or specializes.
-
C.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
D.
careerTackles
Indicates the total number of tackles a player has made over the course of their entire career.
-
E.
careerSafeties
Indicates the total number of safeties a player has recorded over the course of their career.
- 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_69e75a9402fc81909362ca85277c06d9 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f48e02a94c819090718463605abfb5 |
completed | May 1, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69f4683472ec8190a483b3b8afe71720 |
completed | May 1, 2026, 8:45 a.m. |
| PDg | Predicate description generation | batch_69f46d361c348190b5fdfd805ecde01b |
completed | May 1, 2026, 9:07 a.m. |
Created at: April 21, 2026, 1:18 p.m.