Triple
T1987367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Hangover |
E43171
|
entity |
| Predicate | setIn |
P1393
|
FINISHED |
| Object | Nevada |
E2834
|
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: Nevada | Statement: [The Hangover, setIn, Nevada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nevada Context triple: [The Hangover, setIn, Nevada]
-
A.
Nevada
chosen
Nevada is a western U.S. state known for its vast deserts, legalized gambling, and the entertainment hub of Las Vegas.
-
B.
Nevada
Nevada is a small city in western Missouri known as the county seat of Vernon County and for its historic downtown and regional agricultural economy.
-
C.
Nevada, Texas
Nevada, Texas is a small rural city in Collin County known for its quiet residential character within the Dallas–Fort Worth metropolitan area.
-
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 southwestern U.S. state known for its desert climate, the Grand Canyon, and major cities like Phoenix and Tucson.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb840a5708190a9b64564b855fb22 |
completed | March 7, 2026, 5:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae02eda1048190b5452b849315863f |
completed | March 8, 2026, 11:14 p.m. |
Created at: March 4, 2026, 7:37 p.m.