Triple
T15578241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dixie Kong |
E374423
|
entity |
| Predicate | roleInDonkeyKongCountry3 |
P119265
|
FINISHED |
| Object | main protagonist |
—
|
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: main protagonist | Statement: [Dixie Kong, roleInDonkeyKongCountry3, main protagonist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInDonkeyKongCountry3 Context triple: [Dixie Kong, roleInDonkeyKongCountry3, main protagonist]
-
A.
roleInDonkeyKongCountry
Indicates the specific role or function an entity has within the context of Donkey Kong Country.
-
B.
roleInDonkeyKong64
Indicates the role or function an entity has within the context of the game Donkey Kong 64.
-
C.
roleInSuperMarioLand
Indicates the specific function or part an entity plays within the context of the game Super Mario Land.
-
D.
roleInShrekTheThird
Indicates that an entity has a specific role or part in the movie "Shrek the Third."
-
E.
roleInToyStory2
Indicates that an entity has a specific acting or character role in the movie "Toy Story 2."
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e22c89081909b1ec0cd36a1ef45 |
completed | April 16, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69deda817e9881909b0c66fc9056f7d5 |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f05f708190850f1d8782e132b0 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:11 a.m.