Triple
T20687686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lamar Hunt U.S. Open Cup seasons |
E508462
|
entity |
| Predicate | useTiebreakers |
P141055
|
FINISHED |
| Object | extra time |
—
|
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: extra time | Statement: [Lamar Hunt U.S. Open Cup seasons, useTiebreakers, extra time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: useTiebreakers Context triple: [Lamar Hunt U.S. Open Cup seasons, useTiebreakers, extra time]
-
A.
usesHeadToHeadAsTiebreaker
Indicates that a head-to-head comparison between entities is used to break a tie in their ranking or outcome.
-
B.
tiebreaker
Indicates that one entity serves as the deciding factor used to break a tie between two or more otherwise equal options or outcomes.
-
C.
usesGoalDifferenceTiebreaker
Indicates that when entities are tied, their ranking or outcome is decided based on the difference between goals scored and goals conceded.
-
D.
fairPlayTiebreakerAffectedTeams
Indicates that the teams involved were impacted by a tiebreaker decision based on fair play criteria (such as disciplinary records).
-
E.
pointsForTie
Indicates the number of points awarded to each side when a contest or game ends in a tie.
- 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6beaddfe88190897b8963fa8b2245 |
completed | April 21, 2026, 12:02 a.m. |
| PD | Predicate disambiguation | batch_69e5c03caee881908be4dd25796a03d5 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 11:45 a.m.