Triple
T7151262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Troja |
E166695
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Libeň |
E599220
|
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: Libeň | Statement: [Troja, near, Libeň]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Libeň Context triple: [Troja, near, Libeň]
-
A.
Libeň
chosen
Libeň is a district in Prague known for its mix of residential areas, industrial heritage, and major venues such as the O2 Arena.
-
B.
Svitavy
Svitavy is a town in the Czech Republic best known as the birthplace of Oskar Schindler, the industrialist who saved hundreds of Jews during the Holocaust.
-
C.
Říčany
Říčany is a town in the Czech Republic, located just southeast of Prague and known as a popular residential and commuter suburb with historical roots.
-
D.
Broumov
Broumov is a historic town in northeastern Bohemia, Czech Republic, known for its Benedictine monastery and proximity to the Broumov Walls sandstone rock formations.
-
E.
Zličín
Zličín is a district in the western part of Prague that serves as a key transport hub and terminus of a Prague Metro line.
- 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_69c68886779c8190a8e3fbabffe68253 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e7f3e4a88190a3110f2368262528 |
completed | March 27, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cbd551288190a53decc7021929ee |
completed | March 28, 2026, 12:38 p.m. |
Created at: March 27, 2026, 2:46 p.m.