Triple
T12290471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WBA heavyweight title (second reign) |
E292943
|
entity |
| Predicate | reignNumberForFighter |
P104079
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [WBA heavyweight title (second reign), reignNumberForFighter, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reignNumberForFighter Context triple: [WBA heavyweight title (second reign), reignNumberForFighter, 2]
-
A.
fighter1TitleDefenceNumber
Indicates the number of times the first fighter has defended their title.
-
B.
regimentalNumber
Indicates the unique identifying number assigned to a member of a regiment, linking an individual to their specific regimental record.
-
C.
hasFighter
Indicates that an entity possesses, controls, or is associated with a fighter (such as a combatant, combat vehicle, or fighting unit).
-
D.
primaryFighterModel
Indicates that one entity is the main or standard fighter aircraft model associated with another entity (such as a country, air force, or military unit).
-
E.
fighter1RecordBeforeFight
Indicates the win-loss-draw record of the first fighter prior to the specified fight.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9261e1570819084bb4fdb44aa6aea |
completed | April 10, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69d91c4d9a9c8190aeb7beaf9792d8f0 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d9261b7f088190b69fe6961015fce3 |
completed | April 10, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:52 p.m.