Triple
T12566838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Westphalia |
E295497
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Witten-Annen
Witten-Annen is a district of the city of Witten in North Rhine-Westphalia, Germany, known for its residential areas and proximity to the Ruhr industrial region.
|
E990666
|
NE FINISHED |
How this triple was built (4 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: Witten-Annen | Statement: [Province of Westphalia, containsSettlement, Witten-Annen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Witten-Annen Context triple: [Province of Westphalia, containsSettlement, Witten-Annen]
-
A.
Nonnenwerth
Nonnenwerth is a small Rhine River island in Germany known for its historic monastery and scenic location near Bad Honnef.
-
B.
Hawise
Hawise is a medieval European female given name borne by several noblewomen in England and France.
-
C.
Brannenburg
Brannenburg is a Bavarian municipality in southern Germany, known for its scenic Alpine setting and outdoor recreation opportunities.
-
D.
Wittenborn
Wittenborn was a notable mid-20th-century art and design book publisher known for producing influential works in modern art, architecture, and design.
-
E.
Brannan
Brannan is a surname of English and Irish origin borne by various notable individuals, including American politician Charles F. Brannan.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Witten-Annen Triple: [Province of Westphalia, containsSettlement, Witten-Annen]
Generated description
Witten-Annen is a district of the city of Witten in North Rhine-Westphalia, Germany, known for its residential areas and proximity to the Ruhr industrial region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Witten-Annen Target entity description: Witten-Annen is a district of the city of Witten in North Rhine-Westphalia, Germany, known for its residential areas and proximity to the Ruhr industrial region.
-
A.
Nonnenwerth
Nonnenwerth is a small Rhine River island in Germany known for its historic monastery and scenic location near Bad Honnef.
-
B.
Hawise
Hawise is a medieval European female given name borne by several noblewomen in England and France.
-
C.
Brannenburg
Brannenburg is a Bavarian municipality in southern Germany, known for its scenic Alpine setting and outdoor recreation opportunities.
-
D.
Wittenborn
Wittenborn was a notable mid-20th-century art and design book publisher known for producing influential works in modern art, architecture, and design.
-
E.
Brannan
Brannan is a surname of English and Irish origin borne by various notable individuals, including American politician Charles F. Brannan.
- F. None of above. chosen
Provenance (5 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d954a325948190994bcfc9d571a3a8 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f655914f908190afbebbec3cb57e73 |
completed | May 2, 2026, 7:50 p.m. |
| NEDg | Description generation | batch_69f657e504c881909b960acc7758b39d |
completed | May 2, 2026, 8 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f658a80fd08190b1b8c161ca6e56ec |
completed | May 2, 2026, 8:03 p.m. |
Created at: April 8, 2026, 11:49 p.m.