Triple
T5948766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Newcastle-under-Lyme |
E132343
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Rybnik |
E351435
|
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: Rybnik | Statement: [Newcastle-under-Lyme, hasTwinTown, Rybnik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rybnik Context triple: [Newcastle-under-Lyme, hasTwinTown, Rybnik]
-
A.
Rybnik
chosen
Rybnik is a significant industrial and cultural city in the Silesian region of southern Poland, known for its coal mining heritage and regional economic importance.
-
B.
Dąbie
Dąbie is a small town in central Poland, located in the Łódź Voivodeship along the Ner River.
-
C.
Skawina
Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
-
D.
Rybi Potok
Rybi Potok is a mountain stream in the Tatra Mountains of southern Poland that drains the waters of the popular alpine lake Morskie Oko.
-
E.
Bílina
Bílina is a river in the Czech Republic that flows through the Ústí nad Labem Region and is known for passing through several industrial and mining areas.
- 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_69c00869d3308190af89b2453e0f7546 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0397deea08190b9397d0413740300 |
completed | March 22, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16e909f9c8190a78254d81437f404 |
completed | March 23, 2026, 4:47 p.m. |
Created at: March 22, 2026, 4:01 p.m.