Triple
T21808529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lochkov |
E538410
|
entity |
| Predicate | belongsToNUTS3Region |
P9956
|
FINISHED |
| Object | CZ010 Prague |
—
|
NE NERFINISHED |
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: CZ010 Prague | Statement: [Lochkov, belongsToNUTS3Region, CZ010 Prague]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CZ010 Prague Context triple: [Lochkov, belongsToNUTS3Region, CZ010 Prague]
-
A.
CZ010 – Prague
chosen
CZ010 – Prague is the NUTS 3 statistical region corresponding to the capital city of the Czech Republic, Prague.
-
B.
42 Prague
42 Prague is a tuition-free, peer-to-peer programming school in the Czech Republic that follows the innovative, project-based learning model of the international 42 network.
-
C.
Prague-Libeň
Prague-Libeň is a district of Prague, Czech Republic, historically notable as the site of the World War II Operation Anthropoid assassination of Reinhard Heydrich.
-
D.
Prague 10
Prague 10 is one of the administrative districts of Prague, Czech Republic, encompassing mainly residential neighborhoods and parts of the city’s eastern area.
-
E.
Prague 9
Prague 9 is a municipal district of Prague in the Czech Republic, known for its mix of residential areas, industrial zones, and major venues such as large sports and entertainment arenas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c473f0f8819086c9d1b4a143bd67 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f078047ca88190a0efa4bc7f2faf80 |
completed | April 28, 2026, 9:04 a.m. |
Created at: April 16, 2026, 6:53 p.m.