Triple
T6719265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buellton |
E153353
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Solvang |
E166125
|
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: Solvang | Statement: [Buellton, near, Solvang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Solvang Context triple: [Buellton, near, Solvang]
-
A.
Solvang
chosen
Solvang is a Danish-themed tourist town in California known for its Scandinavian architecture, bakeries, and wineries.
-
B.
Vadsø
Vadsø is a small coastal town and administrative center in Finnmark, known for its Arctic location on the Varanger Peninsula and its role as a hub of Sami and Kven culture in Northern Norway.
-
C.
Solør
Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
-
D.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
-
E.
Blangsted
Blangsted is a surname most notably associated with Folmar Blangsted, a film editor.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d135d27c819088c45839ad0e7bab |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7129975fc8190984132e0680b46c6 |
completed | March 27, 2026, 11:28 p.m. |
Created at: March 27, 2026, 2:07 p.m.