Triple
T21410949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delaware |
E528165
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Wilmington |
—
|
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: Wilmington | Statement: [Delaware, largestCity, Wilmington]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilmington Context triple: [Delaware, largestCity, Wilmington]
-
A.
Wilmington
Wilmington is a historic port city in southeastern North Carolina known for its riverfront, nearby Atlantic beaches, and well-preserved downtown district.
-
B.
Wilmington
Wilmington is a suburban town in northeastern Massachusetts, United States, located within the Greater Boston area.
-
C.
Wilmington
Wilmington is a village and civil parish in the Dartford district of Kent, England, situated near the town of Swanley.
-
D.
Wilmington
Wilmington is a historic harbor-area neighborhood in the city of Los Angeles, California, known for its proximity to the Port of Los Angeles and its industrial and maritime character.
-
E.
Wilmington
chosen
Wilmington is the largest city in the U.S. state of Delaware, known as a regional financial and corporate hub.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b1b6595481908cdf915e3585ed08 |
completed | April 22, 2026, 11:32 a.m. |
Created at: April 16, 2026, 5:43 p.m.