Triple
T19507539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benenden School |
E488061
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Benenden |
—
|
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: Benenden | Statement: [Benenden School, locatedIn, Benenden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benenden Context triple: [Benenden School, locatedIn, Benenden]
-
A.
Benenden
chosen
Benenden is a rural village in Kent, England, known for its historic parish church, traditional village green, and the independent girls’ school Benenden School.
-
B.
Benkheim
Benkheim is the former German name for the village now known as Banie Mazurskie in northeastern Poland.
-
C.
Binsfeld
Binsfeld is a small village in northern Luxembourg, situated within the commune of Troisvierges near the borders with Belgium and Germany.
-
D.
Bannout
Bannout is a Lebanese surname most notably associated with Samir Bannout, a former professional bodybuilder and Mr. Olympia champion.
-
E.
Hagenborgh
Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e635130e708190bb3d70e1abbade2a |
completed | April 20, 2026, 2:15 p.m. |
Created at: April 10, 2026, 1:40 p.m.