Triple
T15763412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donkey Kong universe |
E382153
|
entity |
| Predicate | featuresSpecies |
P7733
|
FINISHED |
| Object | Kongs |
E1154594
|
NE 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: Kongs | Statement: [Donkey Kong universe, featuresSpecies, Kongs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kongs Context triple: [Donkey Kong universe, featuresSpecies, Kongs]
-
A.
Kong
Kong is an inverted steel roller coaster at Six Flags Discovery Kingdom known for its multiple inversions and intense, suspended ride experience.
-
B.
Bluster Kong
chosen
Bluster Kong is a pompous, self-important member of the Kong family who appears in the Donkey Kong Country animated series as a wealthy, business-minded Kong.
-
C.
El Pangui
El Pangui is a town in southeastern Ecuador that serves as an administrative and commercial center in the Amazonian province of Zamora-Chinchipe.
-
D.
KONG
KONG is a television station in the Seattle–Tacoma market, commonly associated with KING-TV as its sister station.
-
E.
Kogo
Kogo is a settlement located in the Litoral region of Equatorial Guinea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b6c9fc8190a1bcf763c4b04b12 |
completed | April 16, 2026, 3 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff8776c2488190ad27fd79e2ce4e14 |
completed | May 9, 2026, 7:13 p.m. |
Created at: April 10, 2026, 4:47 a.m.