Triple
T19968231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omar Gonzalez |
E479998
|
entity |
| Predicate | numberOfMLSChampionshipsWithLAGalaxy |
P18818
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Omar Gonzalez, numberOfMLSChampionshipsWithLAGalaxy, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMLSChampionshipsWithLAGalaxy Context triple: [Omar Gonzalez, numberOfMLSChampionshipsWithLAGalaxy, 3]
-
A.
MLS_Cup_titles
chosen
Indicates the number of MLS Cup championship titles that an entity has won.
-
B.
isFirstMLSChampionshipFor
Indicates that the referenced MLS Cup title is the first championship ever won by the specified team.
-
C.
numberOfLigaMXTitles
Indicates the total count of Liga MX championship titles an entity has won.
-
D.
wonMLS Cup
Indicates that the subject secured the championship title in Major League Soccer’s MLS Cup competition.
-
E.
awayTeamPreviousMLS CupFinalAppearances
Indicates the number of times the away team has previously appeared in an MLS Cup final before the current match.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc6b0208190b1ae30be95712326 |
completed | April 20, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69e537f7e4848190b431a69ec3f1b609 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:54 p.m.