Triple
T5812156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Open Cup 2014 |
E128893
|
entity |
| Predicate | SeattleSoundersFCMLSeraTitlesCountAfterWin |
P66469
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [U.S. Open Cup 2014, SeattleSoundersFCMLSeraTitlesCountAfterWin, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: SeattleSoundersFCMLSeraTitlesCountAfterWin Context triple: [U.S. Open Cup 2014, SeattleSoundersFCMLSeraTitlesCountAfterWin, 4]
-
A.
MLS_Cup_titles
Indicates the number of MLS Cup championship titles that an entity has won.
-
B.
awayTeamPreviousMLS CupFinalAppearances
Indicates the number of times the away team has previously appeared in an MLS Cup final before the current match.
-
C.
MLS CupFinalCity
Indicates the city where the MLS Cup final match is held.
-
D.
MLS CupFinalLoser
Indicates the team that lost in the final match of a given MLS Cup competition.
-
E.
wonMLS Cup
Indicates that the subject secured the championship title in Major League Soccer’s MLS Cup competition.
- 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_69c0084788848190bcf71f6bc5d71597 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02b54c2848190bb85212689d0b511 |
completed | March 22, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c021d5ecd081908a62dd66e26f8598 |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c028ffe180819099e084fe557e789c |
completed | March 22, 2026, 5:38 p.m. |
Created at: March 22, 2026, 3:52 p.m.