Triple
T13400936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HC Sparta Praha |
E319824
|
entity |
| Predicate | championshipTitlesApproximateCount |
P48891
|
FINISHED |
| Object | multiple |
—
|
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 | Statement: [HC Sparta Praha, championshipTitlesApproximateCount, multiple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: championshipTitlesApproximateCount Context triple: [HC Sparta Praha, championshipTitlesApproximateCount, multiple]
-
A.
worldChampionshipTitles
Indicates the number of world championship titles an entity has won.
-
B.
associatedChampionshipCount
chosen
Indicates the number of championships that are linked or related to a given entity.
-
C.
numberOfTitleDefenses
Indicates the number of times an entity has successfully defended a previously won title or championship.
-
D.
hasChampionships
Indicates that one entity possesses or has won one or more championships associated with another entity.
-
E.
championshipRecognition
Indicates formal acknowledgment that an entity has won or holds a championship title in a given competition or domain.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbae4982e0819087a9fcb2fa88541f |
completed | April 12, 2026, 2:38 p.m. |
| PD | Predicate disambiguation | batch_69d9a0355de48190bb3fb96912e20df3 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:34 p.m.