Triple
T21296281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Louis, Haut-Rhin |
E524929
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object |
Zillisheim
Zillisheim is a commune in the Haut-Rhin department of northeastern France, situated in the historical region of Alsace.
|
E1475769
|
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: Zillisheim | Statement: [Saint-Louis, Haut-Rhin, twinnedWith, Zillisheim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zillisheim Context triple: [Saint-Louis, Haut-Rhin, twinnedWith, Zillisheim]
-
A.
Ottmarsheim
Ottmarsheim is a commune in northeastern France’s Alsace region, known for its historic Romanesque church and location along the Rhine.
-
B.
Besigheim
Besigheim is a historic wine-growing town in southwestern Germany, known for its well-preserved medieval old town and scenic location between the Enz and Neckar rivers.
-
C.
Kallnach
Kallnach is a municipality in the canton of Bern in Switzerland, located in the Seeland region.
-
D.
Bischheim
Bischheim is a suburban commune in northeastern France, located near Strasbourg in the Bas-Rhin department of the Grand Est region.
-
E.
Waldkirch
Waldkirch is a small historic town in southwestern Germany’s Black Forest region, known for its scenic surroundings and traditional organ-building industry.
- 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: Zillisheim Triple: [Saint-Louis, Haut-Rhin, twinnedWith, Zillisheim]
Generated description
Zillisheim is a commune in the Haut-Rhin department of northeastern France, situated in the historical region of Alsace.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zillisheim Target entity description: Zillisheim is a commune in the Haut-Rhin department of northeastern France, situated in the historical region of Alsace.
-
A.
Ottmarsheim
Ottmarsheim is a commune in northeastern France’s Alsace region, known for its historic Romanesque church and location along the Rhine.
-
B.
Besigheim
Besigheim is a historic wine-growing town in southwestern Germany, known for its well-preserved medieval old town and scenic location between the Enz and Neckar rivers.
-
C.
Kallnach
Kallnach is a municipality in the canton of Bern in Switzerland, located in the Seeland region.
-
D.
Bischheim
Bischheim is a suburban commune in northeastern France, located near Strasbourg in the Bas-Rhin department of the Grand Est region.
-
E.
Waldkirch
Waldkirch is a small historic town in southwestern Germany’s Black Forest region, known for its scenic surroundings and traditional organ-building industry.
- 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7385858ec8190bdc9c5cdcb8d4507 |
completed | April 21, 2026, 8:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09981227a4819095b3ba894f0c6828 |
completed | May 17, 2026, 10:27 a.m. |
| NEDg | Description generation | batch_6a099892435c8190844d6587d1202ff2 |
completed | May 17, 2026, 10:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0999266e0c8190b815cb90d9c984c6 |
completed | May 17, 2026, 10:32 a.m. |
Created at: April 16, 2026, 4:04 p.m.