Triple
T2774087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vincent Enyeama |
E61526
|
entity |
| Predicate | clubHonours |
P41775
|
FINISHED |
| Object | Israeli Premier League titles with Hapoel Tel Aviv and Maccabi Tel Aviv |
—
|
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: Israeli Premier League titles with Hapoel Tel Aviv and Maccabi Tel Aviv | Statement: [Vincent Enyeama, clubHonours, Israeli Premier League titles with Hapoel Tel Aviv and Maccabi Tel Aviv]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clubHonours Context triple: [Vincent Enyeama, clubHonours, Israeli Premier League titles with Hapoel Tel Aviv and Maccabi Tel Aviv]
-
A.
teamHonors
chosen
Indicates that a team has received a particular honor, award, or title.
-
B.
leagueChampionships
Indicates the number of league championship titles an entity has won.
-
C.
LeagueCupTitles
Indicates the number of league cup championships an entity has won.
-
D.
mostDivisionTitlesTeam
Indicates that the subject team holds the record for having won the greatest number of division titles compared to all other teams.
-
E.
teamAchievement
Indicates that a group of individuals collectively accomplished a shared goal or met a performance milestone.
- 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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdcfed608819080988e93df7bdf7c |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.