Triple
T12623818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zihl |
E301457
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object |
Thielle (French)
Thielle is the French name for the Zihl, a river and canal system in western Switzerland connecting Lakes Neuchâtel and Biel.
|
E993180
|
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: Thielle (French) | Statement: [Zihl, hasNameInLanguage, Thielle (French)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thielle (French) Context triple: [Zihl, hasNameInLanguage, Thielle (French)]
-
A.
Tessin (French)
Tessin is the French name for the Swiss canton of Ticino, an Italian-speaking region in southern Switzerland.
-
B.
Louis (French)
Louis is the French given name corresponding to the name Ludwik in other languages.
-
C.
Sarre (French)
Sarre is the French name for the Saar region of western Germany, historically known for its coal industry and strategic location along the French-German border.
-
D.
Lez (French)
Lez (French) is the French name of the Lez River, a watercourse in southern France that flows through the city of Montpellier before reaching the Mediterranean Sea.
-
E.
Auberjonois
Auberjonois is a surname most prominently associated with René Auberjonois, an American actor known for roles in film, television, and voice work.
- 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: Thielle (French) Triple: [Zihl, hasNameInLanguage, Thielle (French)]
Generated description
Thielle is the French name for the Zihl, a river and canal system in western Switzerland connecting Lakes Neuchâtel and Biel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thielle (French) Target entity description: Thielle is the French name for the Zihl, a river and canal system in western Switzerland connecting Lakes Neuchâtel and Biel.
-
A.
Tessin (French)
Tessin is the French name for the Swiss canton of Ticino, an Italian-speaking region in southern Switzerland.
-
B.
Louis (French)
Louis is the French given name corresponding to the name Ludwik in other languages.
-
C.
Sarre (French)
Sarre is the French name for the Saar region of western Germany, historically known for its coal industry and strategic location along the French-German border.
-
D.
Lez (French)
Lez (French) is the French name of the Lez River, a watercourse in southern France that flows through the city of Montpellier before reaching the Mediterranean Sea.
-
E.
Auberjonois
Auberjonois is a surname most prominently associated with René Auberjonois, an American actor known for roles in film, television, and voice work.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9610a897c8190a96f3c78d4b270a2 |
completed | April 10, 2026, 8:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ed8d81c8190baed4292ce3a74a1 |
completed | May 2, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f661e72c2081909b90d849b0449605 |
completed | May 2, 2026, 8:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f662695a348190b9911a19dfc9e779 |
completed | May 2, 2026, 8:45 p.m. |
Created at: April 9, 2026, 5:14 p.m.