Triple
T27018076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frozen III |
E680587
|
entity |
| Predicate | setInFictionalKingdom |
P18263
|
FINISHED |
| Object | Arendelle |
—
|
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: Arendelle | Statement: [Frozen III, setInFictionalKingdom, Arendelle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInFictionalKingdom Context triple: [Frozen III, setInFictionalKingdom, Arendelle]
-
A.
fictionalKingdom
Indicates that an entity is a kingdom that exists only in fiction or imaginative works, rather than in real-world history.
-
B.
setInFictionalizedRegionOf
Indicates that an event or narrative is located within a region that is a fictionalized or altered version of a real-world place.
-
C.
setInFictionalLocation
chosen
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
-
D.
mentionsKingdom
Indicates that one entity explicitly refers to or brings up the topic of a kingdom in relation to another entity.
-
E.
clientKingdomOf
Indicates a relationship where one kingdom is politically subordinate or tributary to another, more powerful kingdom while retaining limited internal autonomy.
- 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_69eeeb5450988190bfc9a3c012ac463a |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c663be481908f233d25d28713a4 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 7:07 a.m.