Triple
T9811791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lawrence Dallaglio |
E238289
|
entity |
| Predicate | leagueTitlesWithWasps |
P67655
|
FINISHED |
| Object | multiple English Premiership titles |
—
|
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 English Premiership titles | Statement: [Lawrence Dallaglio, leagueTitlesWithWasps, multiple English Premiership titles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leagueTitlesWithWasps Context triple: [Lawrence Dallaglio, leagueTitlesWithWasps, multiple English Premiership titles]
-
A.
team2LeagueTitlesContext
Indicates that the second team has won league titles within a specified contextual scope (such as a particular time period, competition, or condition).
-
B.
team2LeagueTitles
Indicates that a given team has won a specified number of league titles.
-
C.
numberOfLeagueTitles
Indicates the total count of league championship titles that an entity has won.
-
D.
mostTitlesTeamTitles
Indicates that the referenced team holds the highest number of titles compared to all other teams in the specified context.
-
E.
teamWithMultipleTitles
chosen
Indicates that a team has won more than one title or championship within the relevant competition or context.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb222ba788190a9085272a3de7852 |
completed | April 2, 2026, 12:02 a.m. |
| PD | Predicate disambiguation | batch_69cd03e01ea881909a7d93fc3994ace5 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:30 p.m.