Triple
T11658132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carl Zeiss |
E277059
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Jena, Germany |
E60682
|
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: Jena, Germany | Statement: [Carl Zeiss, headquartersLocation, Jena, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jena, Germany Context triple: [Carl Zeiss, headquartersLocation, Jena, Germany]
-
A.
Jena
chosen
Jena is a historic university city in the German state of Thuringia, known for its role in optics, philosophy, and science.
-
B.
Friedberg, Germany
Friedberg, Germany is a historic town in the state of Hesse known for its medieval architecture, including a well-preserved castle and old town center.
-
C.
Schröttinghausen, Germany
Schröttinghausen is a small locality in Germany best known as the birthplace of influential astronomer Walter Baade.
-
D.
Linden, Germany
Linden, Germany is a small town in the state of Hesse known for its historical roots and traditional German character.
-
E.
Celle, Germany
Celle is a historic town in Lower Saxony, Germany, known for its well-preserved half-timbered old town and ducal palace.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a3d0331481909682b2e504e4c9a0 |
completed | April 10, 2026, 7:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee882148a08190a1a8c16d8cb7e48a |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 8, 2026, 9:39 p.m.