Triple
T8996596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ismar David |
E214930
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Breslau, Germany |
E238097
|
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: Breslau, Germany | Statement: [Ismar David, birthPlace, Breslau, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Breslau, Germany Context triple: [Ismar David, birthPlace, Breslau, Germany]
-
A.
Breslau
chosen
Breslau is the historical German name for the city now known as Wrocław in southwestern Poland, a major cultural and academic center in Central Europe.
-
B.
Brühl, Germany
Brühl, Germany is a town in North Rhine-Westphalia known for its UNESCO-listed Augustusburg and Falkenlust palaces and its proximity to Cologne.
-
C.
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.
-
D.
Schröttinghausen, Germany
Schröttinghausen is a small locality in Germany best known as the birthplace of influential astronomer Walter Baade.
-
E.
Deggendorf, Germany
Deggendorf, Germany is a Bavarian town on the Danube River known as a regional commercial and industrial center with strong ties to manufacturing and technology 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_69ca83a05c608190bdfdbdb25e994b39 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc68df33c48190a5017426e59c0bc4 |
completed | April 1, 2026, 12:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfd0d586c881909090b424f6fd036f |
completed | April 3, 2026, 2:38 p.m. |
Created at: March 30, 2026, 7:04 p.m.