Triple
T9596404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Küng |
E231540
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Tübingen, Germany |
E306426
|
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: Tübingen, Germany | Statement: [Hans Küng, residence, Tübingen, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tübingen, Germany Context triple: [Hans Küng, residence, Tübingen, Germany]
-
A.
Tübingen, Germany
Tübingen, Germany, is a historic university town in the state of Baden-Württemberg known for its medieval old town and renowned Eberhard Karls University.
-
B.
Tübingen
chosen
Tübingen is a historic university town in southwestern Germany known for its well-preserved medieval old town and prestigious Eberhard Karls University.
-
C.
Würzburg, Germany
Würzburg, Germany is a historic city in northern Bavaria known for its baroque and rococo architecture, prominent university, and renowned Franconian wine culture.
-
D.
Giessen, Germany
Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
-
E.
Bietigheim-Bissingen, Germany
Bietigheim-Bissingen is a town in the German state of Baden-Württemberg known for its strong industrial base and proximity to major automotive and engineering companies.
- 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_69ca8482884481908eccdfdf64d6fbf7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a164c20819093fa863f8f5f79c0 |
completed | April 1, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1792461208190968276ade7c4165d |
completed | April 4, 2026, 8:48 p.m. |
Created at: March 30, 2026, 8:07 p.m.