Triple
T9403630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Eikenberry |
E226535
|
entity |
| Predicate | stateRepresented |
P192
|
FINISHED |
| Object | Washington |
E20479
|
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: Washington | Statement: [Ken Eikenberry, stateRepresented, Washington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Washington Context triple: [Ken Eikenberry, stateRepresented, 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 common English surname most famously borne by George Washington, the first president of the United States.
-
C.
Washington
Washington is a small town in Dutchess County, New York, known for its rural character and the village of Millbrook within its borders.
-
D.
Washington
Washington is a small rural town in Berkshire County in western Massachusetts, known for its forested landscape and quiet, sparsely populated character.
-
E.
Washington
Washington is a rapid transit station on Chicago's 'L' system, formerly serving the CTA Blue Line in the downtown Loop.
- 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_69ca843170f88190800a8ab2b5fc568e |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd51bf7e5c8190850b671778496150 |
completed | April 1, 2026, 5:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1102414e8819097a1bb58a3ded630 |
completed | April 4, 2026, 1:20 p.m. |
Created at: March 30, 2026, 7:46 p.m.