Triple
T781734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hampshire |
E16510
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Eastleigh |
E107646
|
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: Eastleigh | Statement: [Hampshire, contains, Eastleigh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eastleigh Context triple: [Hampshire, contains, Eastleigh]
-
A.
Eastleigh
chosen
Eastleigh is a town in Hampshire, England, situated between Southampton and Winchester and known for its railway heritage and commuter links.
-
B.
Basildon
Basildon is a large town in Essex, England, known as a post-war new town and a major commercial and residential centre within the East of England.
-
C.
Banstead
Banstead is a suburban town in southeast England known for its village-like high street and green spaces within the county of Surrey.
-
D.
Beckenham
Beckenham is a suburban town in southeast London known for its residential character, green spaces, and commuter links into central London.
-
E.
Esher
Esher is a suburban town in the borough of Elmbridge in Surrey, England, known for its affluent residential character and proximity to London.
- 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_69a4936ad1fc81908f190208059ccf78 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a765ba688190ab328bb159583077 |
completed | March 1, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac53753d308190928675f60e27d702 |
completed | March 7, 2026, 4:33 p.m. |
Created at: March 1, 2026, 7:37 p.m.