Triple
T36869284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cap Aurora |
E911180
|
entity |
| Predicate | neighboringResort |
P42027
|
FINISHED |
| Object | Jupiter |
—
|
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: Jupiter | Statement: [Cap Aurora, neighboringResort, Jupiter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: neighboringResort Context triple: [Cap Aurora, neighboringResort, Jupiter]
-
A.
nearbyResortSection
Indicates that one resort section is located close to another resort section in physical proximity.
-
B.
nearbyResortArea
chosen
Indicates that a resort area is located close to or within a short distance of a specified place or entity.
-
C.
countryOfNeighborResort
Indicates that a country is the location in which a neighboring resort of a given entity is situated.
-
D.
associatedResort
Indicates a relationship where one entity is linked or connected to a specific resort, typically as its related or corresponding resort.
-
E.
hasSkiResortNearby
Indicates that one location is situated close enough to another location that it can be considered to have a ski resort in its vicinity.
- 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_69f76e80f6f0819091cba8e19b269615 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fa0a7b00948190a257273d9968c5d7 |
completed | May 5, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69f9fec9c9488190ae2a349651a02782 |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 3, 2026, 4:13 p.m.