Triple
T2594357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thrilla in Manila |
E58193
|
entity |
| Predicate | roundsScheduled |
P11575
|
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: [Thrilla in Manila, roundsScheduled, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roundsScheduled Context triple: [Thrilla in Manila, roundsScheduled, 15]
-
A.
roundCount
chosen
Indicates the number of discrete rounds or iterations that have occurred or are allocated within a process, event, or interaction.
-
B.
roundsFiredEstimate
Indicates an estimated number of shots or rounds that have been fired in a given context or event.
-
C.
hasProperRounds
Indicates that an entity is associated with rounds that meet specified standards or criteria for being considered proper or valid.
-
D.
hasRound
Indicates that an entity possesses, includes, or is associated with a particular round (e.g., a round of an event, game, or process).
-
E.
numberOfTurns
Indicates the total count of discrete turns or rotations involved in an interaction, process, or motion.
- 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_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. |
Created at: March 6, 2026, 9:49 p.m.