Triple
T15069460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donkey Kong Country: Tropical Freeze |
E379836
|
entity |
| Predicate | hasWorldCount |
P117199
|
FINISHED |
| Object | 6 main worlds |
—
|
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: 6 main worlds | Statement: [Donkey Kong Country: Tropical Freeze, hasWorldCount, 6 main worlds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorldCount Context triple: [Donkey Kong Country: Tropical Freeze, hasWorldCount, 6 main worlds]
-
A.
hasWorldNumber
Indicates that an entity is associated with a specific world identified by a particular number.
-
B.
hasWorld
Indicates that an entity possesses, is associated with, or encompasses a particular world or global context.
-
C.
hasNumberOfCountries
Indicates the relationship that specifies how many countries are associated with or contained within a given entity.
-
D.
hasWorldStructure
Indicates that an entity possesses or is characterized by a particular overall world-level organization, framework, or structural configuration.
-
E.
worldNumber
Indicates the specific world, dimension, or universe identifier associated with an entity or event.
- F. None of above. chosen
Provenance (4 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedeebc7e48190a86b4f0afe8844bb |
completed | April 15, 2026, 12:42 a.m. |
| PD | Predicate disambiguation | batch_69deb95a182081908fffc4402b02a394 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:02 a.m.