Triple
T29208970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valentine |
E740494
|
entity |
| Predicate | roleInLordValentinesCastle |
P198286
|
FINISHED |
| Object | 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: protagonist | Statement: [Valentine, roleInLordValentinesCastle, protagonist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInLordValentinesCastle Context triple: [Valentine, roleInLordValentinesCastle, protagonist]
-
A.
roleInNappilyEverAfter
Indicates that an entity has a role or participation in the work "Nappily Ever After."
-
B.
roleInTheNightmareBeforeChristmas
Indicates the specific role or character that an entity has in the movie "The Nightmare Before Christmas."
-
C.
roleInTheGrandBudapestHotel
Indicates that an entity has a specific role or part in the context of "The Grand Budapest Hotel" (such as a character, performer, or production role).
-
D.
roleInBlueValentine
Indicates that an entity has a specific role or involvement in the film "Blue Valentine."
-
E.
roleInLoveEtc
Indicates the specific part or function an entity plays within a romantic or affectionate relationship or situation.
- 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_69f07cb974108190b7e86ca489a6ebb6 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69fed83b1d188190a318b0ad3003200a |
completed | May 9, 2026, 6:46 a.m. |
| PD | Predicate disambiguation | batch_69fed78e03548190b6e6ad93ae8d131d |
completed | May 9, 2026, 6:43 a.m. |
| PDg | Predicate description generation | batch_69fed83a3b8c819092a3bd1ca9d9b38b |
completed | May 9, 2026, 6:46 a.m. |
Created at: April 28, 2026, 12:10 p.m.