Triple
T2594375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thrilla in Manila |
E58193
|
entity |
| Predicate | boutNumberInRivalry |
P40093
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Thrilla in Manila, boutNumberInRivalry, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: boutNumberInRivalry Context triple: [Thrilla in Manila, boutNumberInRivalry, 3]
-
A.
hadRivalryGames
Indicates that there were competitive or rivalrous games or matches played between the entities.
-
B.
hasRivalrySeries
Indicates a recurring competitive relationship or series of contests held between two entities.
-
C.
notableRivalry
Indicates a significant, well-recognized competitive or adversarial relationship between two entities.
-
D.
firstSeasonOfRivalry
Indicates the season in which a particular rivalry between entities first began or was officially recognized.
-
E.
hasRivalryEmotion
Indicates that one entity feels rivalry-based emotions, such as competitive tension or antagonistic comparison, toward another entity.
- 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_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd427f58c8190af1c1a9724158c96 |
completed | March 7, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69abd0d344988190a18dd93b13e002e6 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd2baee308190bdaa41ef1f6bc9cc |
completed | March 7, 2026, 7:24 a.m. |
Created at: March 6, 2026, 9:49 p.m.