Triple
T3867244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince Claus of the Netherlands |
E91890
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Hitzacker
Hitzacker is a small historic town in Lower Saxony, Germany, known for its picturesque setting on the Elbe River and its traditional half-timbered architecture.
|
E396024
|
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: Hitzacker | Statement: [Prince Claus of the Netherlands, placeOfBirth, Hitzacker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hitzacker Context triple: [Prince Claus of the Netherlands, placeOfBirth, Hitzacker]
-
A.
Vogelthal
Vogelthal is a small village in Bavaria, Germany, known as the birthplace of World War II tank commander Michael Wittmann.
-
B.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
C.
Steigerwald
Steigerwald is a forested hill range and nature area in northern Bavaria, Germany, known for its beech forests, vineyards, and traditional Franconian landscapes.
-
D.
Zihl
Zihl is a river in Switzerland that serves as a key tributary within the Aare river system.
-
E.
Wurmberg
Wurmberg is a prominent mountain in the Harz range of central Germany, popular for skiing, hiking, and panoramic views.
- 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: Hitzacker Triple: [Prince Claus of the Netherlands, placeOfBirth, Hitzacker]
Generated description
Hitzacker is a small historic town in Lower Saxony, Germany, known for its picturesque setting on the Elbe River and its traditional half-timbered architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hitzacker Target entity description: Hitzacker is a small historic town in Lower Saxony, Germany, known for its picturesque setting on the Elbe River and its traditional half-timbered architecture.
-
A.
Vogelthal
Vogelthal is a small village in Bavaria, Germany, known as the birthplace of World War II tank commander Michael Wittmann.
-
B.
Weisselberg
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
C.
Steigerwald
Steigerwald is a forested hill range and nature area in northern Bavaria, Germany, known for its beech forests, vineyards, and traditional Franconian landscapes.
-
D.
Zihl
Zihl is a river in Switzerland that serves as a key tributary within the Aare river system.
-
E.
Wurmberg
Wurmberg is a prominent mountain in the Harz range of central Germany, popular for skiing, hiking, and panoramic views.
- 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_69aed9645f348190a9868e7cef56ab7e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec3cc1a88190924125a86f72fd5b |
completed | March 9, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b512410f38819089adccf0a476dd8f |
completed | March 14, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69b512f3504c8190be940148a4f726e9 |
completed | March 14, 2026, 7:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5172b369c8190956d7c54943225cd |
completed | March 14, 2026, 8:07 a.m. |
Created at: March 9, 2026, 3:19 p.m.