Triple
T18911400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | De La Noye |
E462615
|
entity |
| Predicate | canBeAnglicizedAs |
P30815
|
FINISHED |
| Object | Delano |
—
|
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: Delano | Statement: [De La Noye, canBeAnglicizedAs, Delano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Delano Context triple: [De La Noye, canBeAnglicizedAs, Delano]
-
A.
Delano
chosen
Delano is the middle name of Franklin D. Roosevelt, the 32nd president of the United States.
-
B.
Delano
Delano is a small agricultural city in California’s Central Valley known for its table grape production and historic role in the farm labor movement.
-
C.
Delano, Minnesota
Delano, Minnesota is a small city in central Minnesota known for its historic downtown, community events, and location along the South Fork of the Crow River.
-
D.
Davenport
Davenport is a character in the stage play "The Late Christopher Bean," typically portrayed as a visiting art critic or dealer whose arrival helps reveal the true value of the late painter’s work.
-
E.
Davenport
Davenport is an English surname of Norman origin that has been borne by various notable figures in mathematics, politics, and the arts.
- 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c6226f4081909b77aac26c574980 |
completed | April 20, 2026, 6:22 a.m. |
Created at: April 10, 2026, 11:58 a.m.