Triple
T23307252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roger Gracie |
E590482
|
entity |
| Predicate | mmaLossMethod |
P43002
|
FINISHED |
| Object | KO/TKO |
—
|
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: KO/TKO | Statement: [Roger Gracie, mmaLossMethod, KO/TKO]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mmaLossMethod Context triple: [Roger Gracie, mmaLossMethod, KO/TKO]
-
A.
usesLossFunction
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
-
B.
lossType
chosen
Indicates the specific category or nature of a loss associated with an entity or event.
-
C.
estimatedPrincipalLoss
Indicates the amount of principal that is expected to be lost on an investment, loan, or financial position.
-
D.
compressionLoss
Indicates that some amount of information, quality, or fidelity is lost as a result of a compression process.
-
E.
marginOf
Indicates the difference or buffer between two related quantities, values, or boundaries, often expressing how much one exceeds or falls short of another.
- 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_69e25d1c0ecc8190a355aa229f06d0e0 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1972846fc819092ca2b9590b2e177 |
completed | April 29, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69effcf325f88190b320268c3c551abb |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 5:05 p.m.