Triple
T2408375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erlangen Program |
E50327
|
entity |
| Predicate | namedAfter |
P63
|
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 Program, namedAfter, Erlangen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erlangen Context triple: [Erlangen Program, namedAfter, 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.
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.
-
D.
Coburg
Coburg is a historic town in northern Bavaria, Germany, known for its well-preserved medieval architecture and its former role as the seat of the Duchy of Saxe-Coburg and Gotha.
-
E.
Gießen
Gießen is a mid-sized university city in central Germany known for its academic institutions and role as a regional administrative and cultural center.
- 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_69a88b0339a88190a1207333cd271cc9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc92408308190ad2d331ebee71d15 |
completed | March 7, 2026, 6:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24ab711808190a7da65abdc663cb4 |
completed | March 12, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:58 p.m.