Triple
T21702147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milton |
E535683
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Eastney |
—
|
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: Eastney | Statement: [Milton, adjacentTo, Eastney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eastney Context triple: [Milton, adjacentTo, Eastney]
-
A.
Eastney
chosen
Eastney is a coastal suburb located at the eastern end of Portsmouth on Portsea Island in Hampshire, England.
-
B.
Eastleigh
Eastleigh is a town in Hampshire, England, situated between Southampton and Winchester and known for its railway heritage and commuter links.
-
C.
Eastleigh
Eastleigh is a densely populated, commercially vibrant neighborhood in Nairobi, Kenya, known for its large Somali community and bustling markets.
-
D.
East Harbour
East Harbour is the main town and port on the Caribbean island of Utila in Honduras, serving as its central hub for tourism, diving, and local commerce.
-
E.
Haverhill
Haverhill is a market town in Suffolk, England, known for its growing population, light industry, and role as a commuter hub for nearby larger cities.
- 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_69e0c46b44c0819088ab883ebd44e0e8 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef9b8161348190842c433531cfaa5b |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 16, 2026, 6:45 p.m.