Triple
T33284887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Horners |
E852154
|
entity |
| Predicate | locatedInRegionOfOz |
P47688
|
FINISHED |
| Object | northern part of the Land of Oz |
—
|
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: northern part of the Land of Oz | Statement: [Horners, locatedInRegionOfOz, northern part of the Land of Oz]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInRegionOfOz Context triple: [Horners, locatedInRegionOfOz, northern part of the Land of Oz]
-
A.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
-
B.
residesInFictionalLocation
chosen
Indicates that an entity lives or is based in a location that is explicitly fictional or imaginary.
-
C.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
D.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
-
E.
worksAtFictionalPlace
Indicates that an entity is employed at or associated with performing work in a fictional or imaginary location.
- 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_69f349660ff48190a4568803d0b89941 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:32 a.m.