Triple
T56646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cresco, Iowa |
E1120
|
entity |
| Predicate | countrySubdivision |
P766
|
FINISHED |
| Object | State of Iowa |
E15939
|
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: State of Iowa | Statement: [Cresco, Iowa, countrySubdivision, State of Iowa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: State of Iowa Context triple: [Cresco, Iowa, countrySubdivision, State of Iowa]
-
A.
Iowa
chosen
Iowa is a Midwestern U.S. state known for its extensive agriculture, especially corn and soybean production, and its role in national politics through the Iowa caucuses.
-
B.
Nebraska
Nebraska is a landlocked U.S. state on the Great Plains known for its agriculture, prairies, and role as a historic crossroads for westward expansion.
-
C.
Indiana
Indiana is a U.S. state known for its manufacturing base, rich agricultural land, and iconic events like the Indianapolis 500.
-
D.
Illinois
Illinois is a Midwestern U.S. state known for its major metropolis Chicago, diverse economy, and significant political and transportation influence.
-
E.
Wisconsin
Wisconsin is a U.S. state in the Upper Midwest known for its dairy industry, Great Lakes shorelines, and mix of rural landscapes and industrial cities.
- 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_69a248adc5b48190aa8db9fb092fb28a |
completed | Feb. 28, 2026, 1:45 a.m. |
| NER | Named-entity recognition | batch_69a24b07f4a881909e32115e84da02a3 |
completed | Feb. 28, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3cfe1d7ac81909dd6798784c2683a |
completed | March 1, 2026, 5:34 a.m. |
Created at: Feb. 28, 2026, 1:50 a.m.