Triple
T18537116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wallabout |
E452992
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Williamsburg |
—
|
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: Williamsburg | Statement: [Wallabout, locatedNear, Williamsburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Williamsburg Context triple: [Wallabout, locatedNear, 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
Williamsburg is a small village in Sierra County, New Mexico, located near the Rio Grande and close to the city of Truth or Consequences.
-
C.
Williamsburg
chosen
Williamsburg is a trendy Brooklyn neighborhood known for its vibrant arts scene, nightlife, and waterfront views of Manhattan.
-
D.
Williamsburg
Williamsburg is a small rural community located within Dundas County in eastern Ontario, Canada.
-
E.
Williamsburg
Williamsburg is a small town located in Fremont County, Colorado, known for its rural character and proximity to the Rocky Mountains.
- 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5340256d08190bf22d2cb064413b2 |
completed | April 19, 2026, 7:58 p.m. |
Created at: April 10, 2026, 11:37 a.m.