Triple
T21298926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ravenna |
E525002
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Washington |
—
|
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: Washington | Statement: [Ravenna, locatedIn, Washington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Washington Context triple: [Ravenna, locatedIn, Washington]
-
A.
Washington
chosen
Washington is a U.S. state in the Pacific Northwest known for its diverse landscapes, technology industry centered around Seattle, and significant cultural and economic influence on the West Coast.
-
B.
Washington
Washington is a small town in Dutchess County, New York, known for its rural character and the village of Millbrook within its borders.
-
C.
Washington
Washington is a small rural town in Berkshire County in western Massachusetts, known for its forested landscape and quiet, sparsely populated character.
-
D.
Washington
Washington is the given first name of Uruguayan former professional footballer and coach Sebastián "Loco" Abreu.
-
E.
Washington
Washington is the capital city of the United States, known as the center of the federal government and home to numerous national monuments and institutions.
- 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7385a24d08190bfd410c7f10fa6f7 |
completed | April 21, 2026, 8:42 a.m. |
Created at: April 16, 2026, 4:05 p.m.