Triple
T968394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Fe Trail |
E20889
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Colorado |
E42836
|
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: Colorado | Statement: [Santa Fe Trail, locatedIn, Colorado]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colorado Context triple: [Santa Fe Trail, locatedIn, Colorado]
-
A.
Colorado
chosen
Colorado is a landlocked U.S. state known for its Rocky Mountain landscapes, outdoor recreation, and cities like Denver and Boulder.
-
B.
Utah
Utah is a landlocked state in the western United States known for its vast deserts, distinctive red rock landscapes, and prominent national parks such as Zion and Arches.
-
C.
Arizona
Arizona is a southwestern U.S. state known for its desert climate, the Grand Canyon, and major cities like Phoenix and Tucson.
-
D.
New Mexico
New Mexico is a southwestern U.S. state known for its diverse landscapes, rich Native American and Hispanic cultural heritage, and historic cities like Santa Fe and Albuquerque.
-
E.
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.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4481f508190adcf0a965a23862c |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf2ffb088190a49686609d7de213 |
completed | March 8, 2026, 6:25 p.m. |
Created at: March 1, 2026, 7:40 p.m.