Triple
T9859069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franconian Forest |
E239659
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Hof |
E230925
|
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: Hof | Statement: [Franconian Forest, nearbyCity, Hof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hof Context triple: [Franconian Forest, nearbyCity, Hof]
-
A.
Hof
chosen
Hof is a town in northeastern Bavaria, Germany, known for its location near the Czech border and its regional cultural and economic significance.
-
B.
Heiderhof
Heiderhof is a residential subdistrict of the Bonn borough Bad Godesberg in western Germany.
-
C.
Hever
Hever is a village in Kent, England, best known as the location of the historic Hever Castle, former childhood home of Anne Boleyn.
-
D.
Hohberg
Hohberg is a municipality in the Ortenau district of Baden-Württemberg in southwestern Germany.
-
E.
Idstein
Idstein is a historic town in the German state of Hesse, known for its well-preserved medieval old town and timber-framed architecture.
- 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_69ca84e6493081909cf58c8d42ea856b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb39b06b48190ab53ff00ff0513ca |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e43b2de881909e00f6701d1c7b54 |
completed | April 5, 2026, 4:25 a.m. |
Created at: March 30, 2026, 8:35 p.m.