Triple
T24393191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Daneyko |
E614952
|
entity |
| Predicate | spentEntireNHLCareerWith |
P68837
|
FINISHED |
| Object | New Jersey Devils |
—
|
NE NERFINISHED |
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: New Jersey Devils | Statement: [Ken Daneyko, spentEntireNHLCareerWith, New Jersey Devils]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spentEntireNHLCareerWith Context triple: [Ken Daneyko, spentEntireNHLCareerWith, New Jersey Devils]
-
A.
playedEntireNHLCareerFor
chosen
Indicates that a person spent their entire National Hockey League playing career exclusively with a single team.
-
B.
spentEntireMLBCareerWith
Indicates that a player spent their entire Major League Baseball career with a single team.
-
C.
endedNHLCareer
Indicates that one entity caused or marked the conclusion of another entity’s NHL career.
-
D.
spentEntireNflCareerWith
Indicates that an NFL player spent their whole professional NFL career playing for a single team, without ever joining another NFL franchise.
-
E.
playedNHLGamesFor
Indicates that a person has participated in one or more official National Hockey League games as a player for a specified team.
- 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_69e2d7e509b88190a53155d4f3de45ce |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:04 a.m.