Triple
T10103175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hadamar |
E216251
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Biskupiec |
E220538
|
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: Biskupiec | Statement: [Hadamar, twinnedWith, Biskupiec]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biskupiec Context triple: [Hadamar, twinnedWith, Biskupiec]
-
A.
Biskupiec
chosen
Biskupiec is a town in northern Poland known for its location in the picturesque lake district of the Warmian-Masurian Voivodeship.
-
B.
Kazimierz
Kazimierz is a historic district of Kraków known for its rich Jewish heritage, medieval architecture, and vibrant cultural life.
-
C.
Zbyszko
Zbyszko is a Polish given name, traditionally used as a diminutive or variant of Zbigniew and known from medieval and literary contexts.
-
D.
Kiszczak
Kiszczak is a Polish surname most notably associated with Czesław Kiszczak, a communist-era general and interior minister of Poland.
-
E.
Ciechocinek
Ciechocinek is a Polish spa town renowned for its historic saline graduation towers and therapeutic health resorts.
- 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_69ca83d039f08190b9d10363221c69fb |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd09af07c819099774af46ebf62d7 |
completed | April 2, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b6dcca848190851f6f1968fe244c |
completed | April 5, 2026, 7:24 p.m. |
Created at: March 30, 2026, 9:03 p.m.