Triple
T27916336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2008–09 KHL season |
E706083
|
entity |
| Predicate | regulationLossPoints |
P134356
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [2008–09 KHL season, regulationLossPoints, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regulationLossPoints Context triple: [2008–09 KHL season, regulationLossPoints, 0]
-
A.
pointsForRegulationLoss
chosen
Indicates the number of points awarded to a team or player specifically for losing a game in regulation time.
-
B.
usesPointsForLoss
Indicates that a system or rule assigns or deducts points to represent or account for a loss.
-
C.
penaltyPoints
Indicates that a certain number of negative points or demerits are assigned to an entity as a consequence of a rule violation, error, or infraction.
-
D.
regulationScore
Indicates the degree or quality of how well something is controlled, managed, or kept within desired limits.
-
E.
lossType
Indicates the specific category or nature of a loss associated with an entity or event.
- 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_69ef96b6cc808190aab19fb18b235f4b |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f74c70fd248190a9d5543afcb08211 |
completed | May 3, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69f7478e3b548190a51d5d436e2bb036 |
completed | May 3, 2026, 1:03 p.m. |
Created at: April 27, 2026, 6:53 p.m.