Triple
T25093383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Islam (in fictionalized form) |
E628523
|
entity |
| Predicate | associatedWithCharacterTypes |
P47706
|
FINISHED |
| Object | prophets |
—
|
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: prophets | Statement: [Islam (in fictionalized form), associatedWithCharacterTypes, prophets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithCharacterTypes Context triple: [Islam (in fictionalized form), associatedWithCharacterTypes, prophets]
-
A.
associatedWithFilmCharacterType
Indicates that an entity has an association or connection with a particular type or category of film character.
-
B.
associatedWithCharacterFamily
Indicates a relationship where something is linked or connected to a particular character’s family.
-
C.
associatedWithCharacterRole
Indicates that one entity has a connection or linkage to a specific character role played or held by another entity.
-
D.
relatedCharacterType
Indicates that one character has a specified type of relationship or role in connection to another character.
-
E.
associatedWithCharacterGroup
chosen
Indicates that an entity has a connection or affiliation with a particular group of characters.
- 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_69e2ff2f58e881908340527bc5d34f07 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: April 18, 2026, 6:24 a.m.