Triple
T15299389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M2 (Lausanne Metro) |
E365743
|
entity |
| Predicate | servesStation |
P839
|
FINISHED |
| Object |
Sallaz
Sallaz is a metro station on Lausanne’s M2 line in Switzerland, serving the surrounding residential and commercial area.
|
E1149173
|
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: Sallaz | Statement: [M2 (Lausanne Metro), servesStation, Sallaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sallaz Context triple: [M2 (Lausanne Metro), servesStation, Sallaz]
-
A.
Sabally
Sabally is a surname most prominently associated with German-American professional basketball player Satou Sabally of the WNBA.
-
B.
Sassella
Sassella is a renowned subregion of Italy’s Valtellina wine area, best known for its steep terraced vineyards producing distinctive Nebbiolo-based wines.
-
C.
Sarrien
Sarrien is a French surname most notably borne by Ferdinand Sarrien, a politician who served as Prime Minister of France in the early 20th century.
-
D.
Gavisse
Gavisse is a small commune in northeastern France, located in the Moselle department near the border with Luxembourg and Germany.
-
E.
Salleri
Salleri is a town in eastern Nepal that serves as the main commercial and administrative center of the Solukhumbu region, gateway to the Everest area.
- 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: Sallaz Triple: [M2 (Lausanne Metro), servesStation, Sallaz]
Generated description
Sallaz is a metro station on Lausanne’s M2 line in Switzerland, serving the surrounding residential and commercial area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sallaz Target entity description: Sallaz is a metro station on Lausanne’s M2 line in Switzerland, serving the surrounding residential and commercial area.
-
A.
Sabally
Sabally is a surname most prominently associated with German-American professional basketball player Satou Sabally of the WNBA.
-
B.
Sassella
Sassella is a renowned subregion of Italy’s Valtellina wine area, best known for its steep terraced vineyards producing distinctive Nebbiolo-based wines.
-
C.
Sarrien
Sarrien is a French surname most notably borne by Ferdinand Sarrien, a politician who served as Prime Minister of France in the early 20th century.
-
D.
Gavisse
Gavisse is a small commune in northeastern France, located in the Moselle department near the border with Luxembourg and Germany.
-
E.
Salleri
Salleri is a town in eastern Nepal that serves as the main commercial and administrative center of the Solukhumbu region, gateway to the Everest area.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03686bfb8819080ba0caae652170a |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef8513a08190b2d2a7dde85dd43d |
completed | May 9, 2026, 8:25 a.m. |
| NEDg | Description generation | batch_69fef23de4688190beeb59ef43891e3d |
completed | May 9, 2026, 8:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fef2d8fe04819084bb3deb6859d746 |
completed | May 9, 2026, 8:39 a.m. |
Created at: April 10, 2026, 3:15 a.m.