Triple
T32555292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Hone Ropata |
E832080
|
entity |
| Predicate | countryOfFictionalPractice |
P186947
|
FINISHED |
| Object | New Zealand |
—
|
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: New Zealand | Statement: [Dr. Hone Ropata, countryOfFictionalPractice, New Zealand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfFictionalPractice Context triple: [Dr. Hone Ropata, countryOfFictionalPractice, New Zealand]
-
A.
countryOfFictionalContext
Indicates that a work of fiction is primarily set in, or contextually associated with, a particular country.
-
B.
countryOfFictionalRepresentation
chosen
Indicates that one entity is the country in which another entity (such as a work or character) is fictionally set or represented.
-
C.
countryOfOriginFictional
Indicates that a fictional work, character, or element originates from or is associated with a particular country within its narrative or setting.
-
D.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
E.
nationalityOfFictionalSetting
Indicates that a fictional setting is associated with, or belongs to, a particular nationality or country.
- 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_69f34926b9848190ace47d2dd0a0de7c |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a005be4615c8190a710ab704a46c564 |
completed | May 10, 2026, 10:20 a.m. |
| PD | Predicate disambiguation | batch_6a005b8b1cc08190850a392761b84e74 |
completed | May 10, 2026, 10:18 a.m. |
Created at: May 1, 2026, 1:03 a.m.