Triple
T1595487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastern Sierra |
E34272
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mono County |
E26068
|
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: Mono County | Statement: [Eastern Sierra, contains, Mono County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mono County Context triple: [Eastern Sierra, contains, Mono County]
-
A.
Mono County
chosen
Mono County is a sparsely populated county in eastern California known for its dramatic Sierra Nevada landscapes, including parts of Yosemite National Park and Mono Lake.
-
B.
Curry County
Curry County is a rural county in eastern New Mexico known for its agricultural economy and the city of Clovis, a regional hub near the Texas border.
-
C.
Nicholas County
Nicholas County is a largely rural county in central West Virginia known for its mountainous terrain, outdoor recreation areas, and small communities.
-
D.
Cole County
Cole County was the former name of what is now Union County in the southeastern part of South Dakota.
-
E.
Butler County
Butler County is a county in western Pennsylvania, north of Pittsburgh, known for its mix of suburban communities, rural landscapes, and growing industrial and service sectors.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9092ccb388190b2f3ed86b3853651 |
completed | March 5, 2026, 4:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad51b9c6588190810ede38d9e714e2 |
completed | March 8, 2026, 10:38 a.m. |
Created at: March 4, 2026, 7:27 p.m.