Triple
T13834406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Marcos Pass |
E332486
|
entity |
| Predicate | nearbyCommunity |
P4647
|
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: [San Marcos Pass, nearbyCommunity, Solvang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Solvang Context triple: [San Marcos Pass, nearbyCommunity, 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.
Sorø
Sorø is a historic Danish town on the island of Zealand, known for its medieval abbey, prestigious Sorø Academy, and scenic lakeside setting.
-
E.
Vildbjerg
Vildbjerg is a Danish town that serves as the administrative center of the former Trehøje Municipality in the Central Denmark Region.
- 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de029a34bc8190ae892ef7b09fc9e9 |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c0ed7e8c81909ffed37f5b097188 |
completed | May 3, 2026, 9:41 p.m. |
Created at: April 9, 2026, 10:13 p.m.