Triple
T17471639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Route 87 |
E425428
|
entity |
| Predicate | runsThrough |
P416
|
FINISHED |
| Object | Colorado |
—
|
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: Colorado | Statement: [U.S. Route 87, runsThrough, Colorado]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colorado Context triple: [U.S. Route 87, runsThrough, 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.
Colorado
Colorado is a Barbacoan language spoken by indigenous communities in parts of Colombia and Ecuador.
-
C.
D. Colo.
D. Colo. is the standard legal abbreviation for the United States District Court for the District of Colorado, a federal trial court within the Tenth Circuit.
-
D.
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.
-
E.
Arizona
"Arizona" is a 2018 dark comedy thriller film set during the 2009 housing crisis, starring Rosemarie DeWitt and Danny McBride.
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451b8a51081908d94bebe2417e3d3 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:47 a.m.