Triple
T8572866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gigi Simoni |
E202969
|
entity |
| Predicate | leagueTitlesAsCoach |
P51745
|
FINISHED |
| Object | multiple Serie B promotions |
—
|
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: multiple Serie B promotions | Statement: [Gigi Simoni, leagueTitlesAsCoach, multiple Serie B promotions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leagueTitlesAsCoach Context triple: [Gigi Simoni, leagueTitlesAsCoach, multiple Serie B promotions]
-
A.
championshipWonAsCoach
Indicates that the subject, acting in the role of coach, has won a championship title with the associated team or organization.
-
B.
UEFAChampionsLeagueTitlesAsManager
Indicates the number of UEFA Champions League titles a person has won specifically in the role of team manager.
-
C.
leagueChampionshipWonAsManager
chosen
Indicates that the person has won a league championship in the role of a manager for the specified team or organization.
-
D.
leagueTitleWonAsStaff
Indicates that a person, in a non-playing staff role (e.g., coach or manager), was part of a team that won a league title.
-
E.
medalWonAsCoach
Indicates that an individual has won a medal in the role of a coach rather than as a competitor.
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea43843c8190ac2224d427bb7a75 |
completed | March 31, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69cbd11856048190a1ce4b83a38f6965 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:21 p.m.