Triple
T12385516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erlangen-Höchstadt |
E295851
|
entity |
| Predicate | bordersWith |
P224
|
FINISHED |
| Object | Erlangen |
E80092
|
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: Erlangen | Statement: [Erlangen-Höchstadt, bordersWith, Erlangen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erlangen Context triple: [Erlangen-Höchstadt, bordersWith, Erlangen]
-
A.
Erlangen
chosen
Erlangen is a city in northern Bavaria, Germany, known for its university, research institutions, and historical association with mathematician Emmy Noether.
-
B.
Erlangen-Höchstadt
Erlangen-Höchstadt is a rural district in the Bavarian region of Middle Franconia in Germany, known for encompassing towns such as Herzogenaurach and parts of the metropolitan area around Erlangen.
-
C.
Kronach
Kronach is a historic town in northern Bavaria, Germany, known for its well-preserved medieval old town and the imposing Rosenberg Fortress.
-
D.
Heilbronn
Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
-
E.
Coburg
Coburg is a suburb in Melbourne, Australia, known for its diverse community, historic architecture, and access to the city via major tram routes.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbd489c819098233a111442762e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716afa8008190b4c518dd6004d87a |
completed | May 3, 2026, 9:34 a.m. |
Created at: April 8, 2026, 9:54 p.m.