Triple
T4498879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nuremberg metropolitan region |
E100767
|
entity |
| Predicate | hasCity |
P316
|
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: [Nuremberg metropolitan region, hasCity, Hof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hof Context triple: [Nuremberg metropolitan region, hasCity, 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.
Hever
Hever is a village in Kent, England, best known as the location of the historic Hever Castle, former childhood home of Anne Boleyn.
-
C.
Idstein
Idstein is a historic town in the German state of Hesse, known for its well-preserved medieval old town and timber-framed architecture.
-
D.
Mindelheim
Mindelheim is a historic town in Bavaria, Germany, known for its well-preserved medieval old town and former status as a princely seat.
-
E.
Lilienthal
Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
- 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_69bd43cdf15081909a4fa2585ff63b3e |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56c29868819097633c7cd398e865 |
completed | March 20, 2026, 2:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd6f89114081908adbd8e78d4c8ec4 |
completed | March 20, 2026, 4:02 p.m. |
Created at: March 20, 2026, 1 p.m.