Triple
T8295616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Maria Delano |
E194207
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Delano |
E123
|
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: Delano | Statement: [Susan Maria Delano, familyName, Delano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Delano Context triple: [Susan Maria Delano, familyName, 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.
Davenport
Davenport is an English surname of Norman origin that has been borne by various notable figures in mathematics, politics, and the arts.
-
D.
Davenport
Davenport is a historic community in Toronto, Ontario, known for its early settlement along Davenport Road and its role in the city’s industrial and residential development.
-
E.
de Kalb
De Kalb is a German-born French military officer who served as a major general in the Continental Army during the American Revolutionary War and became a symbol of foreign support for the American cause.
- 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_69ca82e50ebc81909aa7b260c76bd757 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7df73d4c81909ad9cf0786eb5a20 |
completed | March 31, 2026, 7:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd952e038c819090023cbcdab1e3ae |
completed | April 1, 2026, 9:59 p.m. |
Created at: March 30, 2026, 5:53 p.m.