Triple
T14932643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Hellmut Kirst |
E372306
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Ostróda, Poland |
E376925
|
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: Ostróda, Poland | Statement: [Hans Hellmut Kirst, placeOfBirth, Ostróda, Poland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ostróda, Poland Context triple: [Hans Hellmut Kirst, placeOfBirth, Ostróda, Poland]
-
A.
Ustronie, Poland
Ustronie is a locality in Poland known historically as the place where the renowned Polish violinist and composer Karol Lipiński died.
-
B.
Ostróda
chosen
Ostróda is a town in northern Poland known for its lakeside setting, tourism, and role as a local economic and cultural center.
-
C.
Lipno, Poland
Lipno, Poland is a small town in north-central Poland’s Kuyavian-Pomeranian Voivodeship, known as the birthplace of prominent economist and reformer Leszek Balcerowicz.
-
D.
Kozienice, Poland
Kozienice is a historic town in east-central Poland known for its location along the Vistula River and proximity to the Kozienice Landscape Park.
-
E.
Tychy, Poland
Tychy, Poland is an industrial city in the Silesian region known for its major automotive manufacturing plants and brewing industry.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded646a0808190ba5c0c91bde011c5 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e8ac6d08190809045a6d00a3d47 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:37 a.m.