Triple
T20037643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Everett Greenbaum |
E497318
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Everett |
—
|
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: Everett | Statement: [Everett Greenbaum, givenName, Everett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Everett Context triple: [Everett Greenbaum, givenName, Everett]
-
A.
Everett
Everett is a city in Middlesex County, Massachusetts, located just north of Boston and known for its industrial history and urban residential character.
-
B.
Everett
Everett is a city in western Washington State, known as a major industrial and maritime hub north of Seattle and home to a large Boeing aircraft assembly plant.
-
C.
Everett
chosen
Everett is a surname of English origin borne by various notable individuals, including American politician and orator Edward Everett.
-
D.
Tacoma
Tacoma is a small suburb on the Central Coast of New South Wales, Australia, situated along the Wyong River near Tuggerah Lake.
-
E.
Bremerton
Bremerton is a waterfront city in Washington State known for its naval shipyard and ferry connection to Seattle across Puget Sound.
- 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e935ac8190900cdb4f0cfde505 |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:36 p.m.