Triple
T23041308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hellingly Hospital |
E573743
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Hellingly |
—
|
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: Hellingly | Statement: [Hellingly Hospital, locatedIn, Hellingly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hellingly Context triple: [Hellingly Hospital, locatedIn, Hellingly]
-
A.
Hellingly
chosen
Hellingly is a village and civil parish in East Sussex, England, known for its rural character and historic parish church.
-
B.
Hollingen
Hollingen is a coastal village in Aukra Municipality in Møre og Romsdal county, Norway, known as a local ferry and transport hub.
-
C.
Tintinhull
Tintinhull is a small rural village in Somerset, England, noted for its historic stone cottages and the National Trust-owned Tintinhull Garden.
-
D.
Huguley
Huguley is an unincorporated community and census-designated place located in Chambers County, Alabama, United States.
-
E.
Laleham
Laleham is a riverside village in Surrey, England, situated on the River Thames and known for its historic church, green spaces, and residential character.
- 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_69e245b9c11481909d06c872214d21af |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18513fc54819096d761a0be75b774 |
completed | April 29, 2026, 4:12 a.m. |
Created at: April 17, 2026, 3:53 p.m.