Triple
T19829555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rip Wheeler |
E476418
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Montana |
—
|
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: Montana | Statement: [Rip Wheeler, setting, Montana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montana Context triple: [Rip Wheeler, setting, Montana]
-
A.
Montana
chosen
Montana is a large, sparsely populated U.S. state in the northern Rocky Mountains known for its expansive wilderness, national parks like Glacier, and wide-open "Big Sky" landscapes.
-
B.
Montana
Montana is a small city in northwestern Bulgaria known as a regional administrative and economic center near the foothills of the Balkan Mountains.
-
C.
Montana
Montana was a former Swiss alpine resort municipality that later became part of the larger resort area of Crans-Montana in the canton of Valais.
-
D.
Wyoming
Wyoming is a sparsely populated U.S. state known for its vast plains, the Rocky Mountains, and iconic national parks like Yellowstone and Grand Teton.
-
E.
Wyoming
Wyoming is a small borough in Luzerne County, Pennsylvania, situated in the historic Wyoming Valley along the Susquehanna River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e656ccd3748190adeaed9a431f8979 |
completed | April 20, 2026, 4:39 p.m. |
Created at: April 10, 2026, 1:50 p.m.