Triple
T8615678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L |
E204029
|
entity |
| Predicate | neighborhoodServed |
P82
|
FINISHED |
| Object | Williamsburg |
E44832
|
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: Williamsburg | Statement: [L, neighborhoodServed, Williamsburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Williamsburg Context triple: [L, neighborhoodServed, Williamsburg]
-
A.
Williamsburg
Williamsburg is a historic colonial city in Virginia renowned for its well-preserved 18th-century architecture and living-history museum, Colonial Williamsburg.
-
B.
Williamsburg
chosen
Williamsburg is a trendy Brooklyn neighborhood known for its vibrant arts scene, nightlife, and waterfront views of Manhattan.
-
C.
Williamsburg
Williamsburg is a small rural community located within Dundas County in eastern Ontario, Canada.
-
D.
Charles City
Charles City is a small northeastern Iowa community known historically as a manufacturing and transportation hub along the Cedar River.
-
E.
Roanoke
Roanoke is a city in Denton County, Texas, known for its family-friendly attractions, including the Hawaiian Falls water park and a vibrant dining scene.
- 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_69ca832ceab8819096e4a9f546695079 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4703b57c81909511de72fa5c38d7 |
completed | March 31, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf5148add48190bd2849d607e46c77 |
completed | April 3, 2026, 5:34 a.m. |
Created at: March 30, 2026, 6:25 p.m.