Triple
T3542863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christiane Nüsslein-Volhard |
E74926
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Magdeburg |
E240455
|
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: Magdeburg | Statement: [Christiane Nüsslein-Volhard, placeOfBirth, Magdeburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magdeburg Context triple: [Christiane Nüsslein-Volhard, placeOfBirth, Magdeburg]
-
A.
Magdeburg
chosen
Magdeburg is a historic city in central Germany, known for its medieval cathedral, role as a major trading and industrial center, and location on the Elbe River.
-
B.
Leipzig
Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
-
C.
Erfurt
Erfurt is a historic German city in the state of Thuringia, known for its well-preserved medieval old town and as an important cultural and educational center.
-
D.
Cottbus
Cottbus is a city in eastern Germany known as a regional center for science and technology, including aerospace research.
-
E.
Schwerin
Schwerin is a historic city in northern Germany known for its picturesque lakeside setting and landmark Schwerin Castle.
- 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_69ad85d274cc8190ab59c97298a1cfbf |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbf752dd481909226044ffe595338 |
completed | March 8, 2026, 6:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be89b0441881908b87c7a62434e79a |
completed | March 21, 2026, 12:06 p.m. |
Created at: March 8, 2026, 3:20 p.m.