Triple
T35605629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. James Atherton |
E1028879
|
entity |
| Predicate | countryOfFictionalFieldwork |
P133191
|
FINISHED |
| Object | Venezuela |
E27092
|
NE 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: Venezuela | Statement: [Dr. James Atherton, countryOfFictionalFieldwork, Venezuela]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfFictionalFieldwork Context triple: [Dr. James Atherton, countryOfFictionalFieldwork, Venezuela]
-
A.
countryOfFictionalContext
Indicates that a work of fiction is primarily set in, or contextually associated with, a particular country.
-
B.
countryOfFictionalRepresentation
Indicates that one entity is the country in which another entity (such as a work or character) is fictionally set or represented.
-
C.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
D.
nationalityOfFictionalSetting
Indicates that a fictional setting is associated with, or belongs to, a particular nationality or country.
-
E.
associatedWithCountryInFiction
chosen
Indicates a fictional relationship in which an entity is linked or connected to a particular country within a fictional context or narrative.
- F. None of above.
Provenance (4 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_69f76e0653ec81909b1b813c126c6574 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a01e4ba62748190a4756689219b6269 |
completed | May 11, 2026, 2:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3868449a5c8190881b0b9bf182d299 |
completed | June 21, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_6a01e3fc2d908190a075c7c1549e5640 |
completed | May 11, 2026, 2:13 p.m. |
Created at: May 3, 2026, 4:05 p.m.