Triple
T4535686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sunderland, United Kingdom |
E107401
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Essen, Germany |
E311580
|
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: Essen, Germany | Statement: [Sunderland, United Kingdom, hasTwinTown, Essen, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essen, Germany Context triple: [Sunderland, United Kingdom, hasTwinTown, Essen, Germany]
-
A.
Essen
chosen
Essen is a major industrial and cultural city in western Germany, historically known as a coal and steel center and now home to several large corporations and universities.
-
B.
Hamm, Germany
Hamm is a city in the German state of North Rhine-Westphalia, known as an industrial and transportation hub in the eastern Ruhr area.
-
C.
Friedberg, Germany
Friedberg, Germany is a historic town in the state of Hesse known for its medieval architecture, including a well-preserved castle and old town center.
-
D.
Minden, Germany
Minden, Germany is a historic town in North Rhine-Westphalia known for its strategic location on the Weser River and its role in significant military events such as the Battle of Minden.
-
E.
Krefeld, Germany
Krefeld, Germany is an industrial city in North Rhine-Westphalia known historically for its textile and silk production.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57b634b08190845d04213cf8d5b9 |
completed | March 20, 2026, 2:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6f8abc3481909dbb42ddde2729ca |
completed | March 21, 2026, 10:14 a.m. |
Created at: March 20, 2026, 1:04 p.m.