Triple
T11133157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Curwood |
E263338
|
entity |
| Predicate | proximityToSettlement |
P3883
|
FINISHED |
| Object | near L’Anse, Michigan |
—
|
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: near L’Anse, Michigan | Statement: [Mount Curwood, proximityToSettlement, near L’Anse, Michigan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToSettlement Context triple: [Mount Curwood, proximityToSettlement, near L’Anse, Michigan]
-
A.
hasNearestLargerSettlement
Indicates that one settlement is associated with the geographically closest settlement that is larger in size or population.
-
B.
hasMunicipalitySeatNearby
Indicates that the municipality’s administrative seat is located in close proximity to the referenced place or entity.
-
C.
proximityToLandmark
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
D.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
E.
hasNearbyTown
chosen
Indicates that one location has a town situated close to it in geographic proximity.
- 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_69d6aa9c0ba08190bbd19c217489b755 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8347a248190837e8c26f25f553a |
completed | April 9, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69d75ce104908190b6cc31ef2f67846a |
completed | April 9, 2026, 8:01 a.m. |
Created at: April 8, 2026, 9:28 p.m.