Triple
T32585619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legion of Boom |
E832912
|
entity |
| Predicate | opponentInKeyGame |
P18497
|
FINISHED |
| Object | Denver Broncos |
—
|
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: Denver Broncos | Statement: [Legion of Boom, opponentInKeyGame, Denver Broncos]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: opponentInKeyGame Context triple: [Legion of Boom, opponentInKeyGame, Denver Broncos]
-
A.
opponentInCase
Indicates that two parties are on opposing sides in the same legal case or proceeding.
-
B.
wasOpponentOf
Indicates that one entity competed or conflicted against another as an adversary in some contest, game, or confrontation.
-
C.
keyOpponents
Indicates that the subject has primary or most significant opponents identified by the object.
-
D.
opponentInScenario
Indicates that one entity is an adversary or rival of another within a specific scenario, context, or situation.
-
E.
facedOpponent
chosen
Indicates that one entity directly confronted or competed against another as an opponent in a contest, conflict, or challenge.
- 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_69f34929ff648190aded9424aa7564ae |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6e02ba6b881908dfafc52d3b75f1c |
completed | May 3, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69f6de09c2f481909f8b2545d3208c9f |
completed | May 3, 2026, 5:32 a.m. |
Created at: May 1, 2026, 1:04 a.m.