Triple
T28612201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klondike |
E724191
|
entity |
| Predicate | hasScoringVariant |
P115017
|
FINISHED |
| Object | standard scoring |
—
|
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: standard scoring | Statement: [Klondike, hasScoringVariant, standard scoring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScoringVariant Context triple: [Klondike, hasScoringVariant, standard scoring]
-
A.
hasScorer
Indicates that one entity serves as the scorer (e.g., the one who scores points, goals, or evaluations) in relation to another entity.
-
B.
hasScoringUnit
Indicates that one entity possesses or is associated with a unit used for scoring, measurement, or point allocation in a given context.
-
C.
hasScoreType
Indicates that an entity is associated with a particular type or category of score.
-
D.
hasScoreCharacteristic
chosen
Indicates that one entity possesses or is associated with a particular scoring-related characteristic or property.
-
E.
hasRankVariant
Indicates that one entity is an alternative or variant form of another entity’s rank or hierarchical level.
- 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_69f01d816d7c8190a1fe27e3434041dc |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 28, 2026, 4:30 a.m.