Triple
T4368434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chalonnes-sur-Loire |
E98834
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object |
Hettstadt
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
|
E470527
|
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: Hettstadt | Statement: [Chalonnes-sur-Loire, twinnedWith, Hettstadt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hettstadt Context triple: [Chalonnes-sur-Loire, twinnedWith, Hettstadt]
-
A.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
-
B.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
C.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
D.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
E.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
- 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: Hettstadt Triple: [Chalonnes-sur-Loire, twinnedWith, Hettstadt]
Generated description
Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hettstadt Target entity description: Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
-
A.
Vellinghausen
Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
-
B.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
C.
Wilhelmsruh
Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
-
D.
Hubersdorf
Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
-
E.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352034d3881909ed4b2f9eef5e823 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be43821d5c8190b50e3eadabf2845b |
completed | March 21, 2026, 7:06 a.m. |
| NEDg | Description generation | batch_69be45b95ab48190b5d8b84c56b1a0ac |
completed | March 21, 2026, 7:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be46e400cc8190aaa7fc42713f30c6 |
completed | March 21, 2026, 7:21 a.m. |
Created at: March 12, 2026, 11:17 p.m.