Triple
T23452830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lippstadt |
E567830
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Uden |
—
|
NE NERFINISHED |
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: Uden | Statement: [Lippstadt, hasTwinTown, Uden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uden Context triple: [Lippstadt, hasTwinTown, Uden]
-
A.
Uden
chosen
Uden is a town in the southern Netherlands known for its location in the province of North Brabant and its proximity to nature reserves and regional industry.
-
B.
Unnan
Unnan is a city in Shimane Prefecture, Japan, known for its rural landscapes, hot springs, and traditional cultural sites.
-
C.
Utne
Utne is a small village in western Norway known for its scenic location along the Hardangerfjord and its historic wooden hotel.
-
D.
Undén
Undén is a Swedish surname most notably associated with Östen Undén, a prominent 20th-century Swedish politician and jurist.
-
E.
Udelnaya
Udelnaya is a residential neighborhood and railway station area in the northern part of Saint Petersburg, Russia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2458b4c888190b1d7998f9862a558 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a694f19081909117bc9b10ca8a83 |
completed | April 29, 2026, 6:35 a.m. |
Created at: April 17, 2026, 5:52 p.m.