Triple
T33659137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Kalvan of Otherwhen |
E862302
|
entity |
| Predicate | hasFictionalTitleOfProtagonist |
P199489
|
FINISHED |
| Object | Lord Kalvan |
—
|
NE NERFINISHED |
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: Lord Kalvan | Statement: [Lord Kalvan of Otherwhen, hasFictionalTitleOfProtagonist, Lord Kalvan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalTitleOfProtagonist Context triple: [Lord Kalvan of Otherwhen, hasFictionalTitleOfProtagonist, Lord Kalvan]
-
A.
hasFictionalLeadCharacter
Indicates that a creative work features a particular fictional character as its main or leading protagonist.
-
B.
isGivenNameOfFictionalCharacter
Indicates that a given name is the personal name borne by a fictional character.
-
C.
hasProtagonistNameInFilmAdaptation
Indicates that a specific name is used for the story’s main character in a particular film adaptation.
-
D.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
E.
hasFictionalAlias
Indicates that an entity is known by an alternative name or identity within a fictional context.
- 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_69f349840ba881908e3bfce536aeb92b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff3e1762d8819089a60e402e682817 |
completed | May 9, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69ff3d8c6f308190a0646b1432752eb8 |
completed | May 9, 2026, 1:58 p.m. |
| PDg | Predicate description generation | batch_69ff3e16527c81908c8d89da704ce012 |
completed | May 9, 2026, 2 p.m. |
Created at: May 1, 2026, 1:42 a.m.