Triple
T27116764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Charlotte King |
E686869
|
entity |
| Predicate | isFictionalCharacterFrom |
P50141
|
FINISHED |
| Object | United States television |
—
|
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: United States television | Statement: [Dr. Charlotte King, isFictionalCharacterFrom, United States television]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFictionalCharacterFrom Context triple: [Dr. Charlotte King, isFictionalCharacterFrom, United States television]
-
A.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
B.
fictionalCharacter
chosen
Indicates that one entity is a fictional character that appears within the narrative world of another entity (such as a work, series, or franchise).
-
C.
isGivenNameOfFictionalCharacter
Indicates that a given name is the personal name borne by a fictional character.
-
D.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
-
E.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
- 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_69ef148c2b588190afc15b529f7af845 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f62440351081908f74fef3c86d282a |
completed | May 2, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69f61b40f02081909bd9c3ea73249163 |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 8:57 a.m.