Triple
T19428849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dashanana |
E486055
|
entity |
| Predicate | associatedCharacterType |
P100869
|
FINISHED |
| Object | rakshasa |
—
|
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: rakshasa | Statement: [Dashanana, associatedCharacterType, rakshasa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCharacterType Context triple: [Dashanana, associatedCharacterType, rakshasa]
-
A.
relatedCharacterType
Indicates that one character has a specified type of relationship or role in connection to another character.
-
B.
typeOfCharacter
Indicates that one entity is a specific kind or category of character in relation to another entity.
-
C.
associatedWithFilmCharacterType
chosen
Indicates that an entity has an association or connection with a particular type or category of film character.
-
D.
helpsCharacterType
Indicates that one character type provides assistance or support to another character type.
-
E.
portrayedByCharacterType
Indicates that an entity is depicted or represented by a character of a specified type (e.g., hero, villain, sidekick) in a narrative or media work.
- F. None of above.
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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6321b78d08190b86cef7c60cbb61c |
completed | April 20, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69e4fd6e806081909053f325ba01ab6b |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:37 p.m.