Triple
T24489543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US Open women’s doubles champion |
E617603
|
entity |
| Predicate | tieBreakRule |
P141055
|
FINISHED |
| Object | standard tiebreak in sets |
—
|
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: standard tiebreak in sets | Statement: [US Open women’s doubles champion, tieBreakRule, standard tiebreak in sets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tieBreakRule Context triple: [US Open women’s doubles champion, tieBreakRule, standard tiebreak in sets]
-
A.
tiebreaker
Indicates that one entity serves as the deciding factor used to break a tie between two or more otherwise equal options or outcomes.
-
B.
tieBreakerMetric
Indicates a metric used to resolve ties when primary comparison criteria result in equal values.
-
C.
useTiebreakers
chosen
Indicates that when primary criteria result in a tie, additional predefined rules or factors are applied to determine a winner or ordering.
-
D.
tiebreakerGameLoser
Indicates the player or team that lost a specific tiebreaker game used to resolve a tie in a competition or match.
-
E.
usesHeadToHeadAsTiebreaker
Indicates that a head-to-head comparison between entities is used to break a tie in their ranking or outcome.
- 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_69e2d7f4e6bc8190aec540ae3b9ed7f2 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:22 a.m.