Triple
T1121034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marián Hossa |
E24610
|
entity |
| Predicate | numberOfStanleyCups |
P3779
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Marián Hossa, numberOfStanleyCups, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStanleyCups Context triple: [Marián Hossa, numberOfStanleyCups, 3]
-
A.
mostStanleyCupsInNHL
Indicates having won the greatest number of Stanley Cup championships in the history of the NHL compared to all other entities.
-
B.
StanleyCupChampionships
chosen
Indicates the number of times an entity has won the Stanley Cup championship.
-
C.
StanleyCupWinningSeasons
Indicates the seasons in which a team won the Stanley Cup championship.
-
D.
StanleyCupWinnerWith
Indicates that one entity is the winner of the Stanley Cup in association with, or while being a member of, the other entity (typically a team or organization).
-
E.
StanleyCupWonWith
Indicates that a person or team won the Stanley Cup while being a member of or associated with a specific 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bc4bc21881909dcfe628f59f3e8c |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4562f48190831e959f5f309956 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:43 p.m.