Triple
T1306220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2003 UEFA Champions League Final |
E27883
|
entity |
| Predicate | ACMilanTitleCountAfterMatch |
P15090
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [2003 UEFA Champions League Final, ACMilanTitleCountAfterMatch, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ACMilanTitleCountAfterMatch Context triple: [2003 UEFA Champions League Final, ACMilanTitleCountAfterMatch, 6]
-
A.
numberOfCoppaItaliaTitles
Indicates the quantity of Coppa Italia titles that an entity has won.
-
B.
numberOfSupercoppaItalianaTitles
Indicates the count of Supercoppa Italiana titles that an entity has won.
-
C.
homeTeamTitleCountAfterMatch
Indicates the total number of titles the home team has accumulated after the completion of the referenced match.
-
D.
UEFAChampionsLeagueTitles
chosen
Indicates the number of UEFA Champions League titles an entity (typically a football club) has won.
-
E.
MLS_Cup_titles
Indicates the number of MLS Cup championship titles that an entity has won.
- 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c15490a88190872c3d2698a8f9c9 |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4bee9e4a88190b22ab2ee831a23c9 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.