Triple
T12880272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jan Patočka |
E308074
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Turnov |
E830506
|
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: Turnov | Statement: [Jan Patočka, placeOfBirth, Turnov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Turnov Context triple: [Jan Patočka, placeOfBirth, Turnov]
-
A.
Turnov
chosen
Turnov is a historic town in the northern Czech Republic, known as a gateway to the Bohemian Paradise region and for its traditional gemstone cutting and jewelry-making.
-
B.
Tatura
Tatura is a rural town in northern Victoria, Australia, known for its agricultural industries and historical World War II internment camps.
-
C.
Vác
Vác is a historic town on the Danube in northern Hungary, known for its Baroque architecture and role as a regional cultural and religious center.
-
D.
Švihov
Švihov is a small town in the Plzeň Region of the Czech Republic, known for its well-preserved water castle and its location on the Úhlava River.
-
E.
Tuřany
Tuřany is a district of the Czech city of Brno, known for hosting the region’s main international airport.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fc1e488190a0c48039f6213e62 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a55393a88190a88c9357a6db5aec |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:39 p.m.