Triple
T5377559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayreuth |
E112998
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Prague 6 |
E287726
|
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: Prague 6 | Statement: [Bayreuth, twinTown, Prague 6]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prague 6 Context triple: [Bayreuth, twinTown, Prague 6]
-
A.
Prague 6
chosen
Prague 6 is a large municipal district of Prague, Czech Republic, known for its residential neighborhoods, diplomatic quarter, and proximity to Prague Castle and the airport.
-
B.
Jičín
Jičín is a historic town in the Czech Republic known for its well-preserved medieval center and association with the fairy-tale character Rumcajs.
-
C.
Nymburk
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
-
D.
Kolín
Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
-
E.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
- 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_69bd4436a1988190af18dcff7fd306b4 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd86cb13ac81909dc364e7d3605844 |
completed | March 20, 2026, 5:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bff2ad363481908f3f287052233ae8 |
completed | March 22, 2026, 1:46 p.m. |
Created at: March 20, 2026, 2:03 p.m.