Triple
T3838243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winterthur |
E93387
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Pilsen |
E106758
|
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: Pilsen | Statement: [Winterthur, hasTwinTown, Pilsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pilsen Context triple: [Winterthur, hasTwinTown, Pilsen]
-
A.
Pilsen
chosen
Pilsen is a city in the Czech Republic best known as the birthplace of Pilsner beer, a pale lager style that became one of the world’s most popular.
-
B.
Orel
Orel is a male given name most famously associated with former Major League Baseball pitcher Orel Hershiser.
-
C.
Cleves
Cleves is a historic town in western Germany near the Dutch border, known for its medieval castle and role as a former ducal capital in the Lower Rhine region.
-
D.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
-
E.
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.
- 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_69aed96ce578819084ab16e3439976c9 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeb9d11f081909fc51e84657ec7f1 |
completed | March 9, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5040835dc81909ecf5053128f1cc7 |
completed | March 14, 2026, 6:45 a.m. |
Created at: March 9, 2026, 3:18 p.m.