Triple

T2476895
Position Surface form Disambiguated ID Type / Status
Subject Lake Annecy E55108 entity
Predicate nearMountain P31783 FINISHED
Object Semnoz
Semnoz is a mountain in the French Alps known for its panoramic views over Lake Annecy and its popular hiking, skiing, and cycling routes.
 E269844 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: Semnoz | Statement: [Lake Annecy, nearMountain, Semnoz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Semnoz
Context triple: [Lake Annecy, nearMountain, Semnoz]
  • A. Zezuru
    Zezuru is a major dialect of the Shona language spoken primarily in central and northern Zimbabwe.
  • B. Zimeysa
    Zimeysa is a railway station in the canton of Geneva, Switzerland, serving local and regional train services on the Geneva–La Plaine line.
  • C. Hamutal
    Hamutal was a queen of Judah, known as the mother of the last king of Judah, Zedekiah, during the final years before the Babylonian exile.
  • D. Myaso
    Myaso is a colloquial nickname used by fans and rivals to refer to the Russian football club Spartak Moscow, reflecting its historical association with the meat industry.
  • E. Yunaska
    Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
  • 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: Semnoz
Triple: [Lake Annecy, nearMountain, Semnoz]
Generated description
Semnoz is a mountain in the French Alps known for its panoramic views over Lake Annecy and its popular hiking, skiing, and cycling routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Semnoz
Target entity description: Semnoz is a mountain in the French Alps known for its panoramic views over Lake Annecy and its popular hiking, skiing, and cycling routes.
  • A. Zezuru
    Zezuru is a major dialect of the Shona language spoken primarily in central and northern Zimbabwe.
  • B. Zimeysa
    Zimeysa is a railway station in the canton of Geneva, Switzerland, serving local and regional train services on the Geneva–La Plaine line.
  • C. Hamutal
    Hamutal was a queen of Judah, known as the mother of the last king of Judah, Zedekiah, during the final years before the Babylonian exile.
  • D. Myaso
    Myaso is a colloquial nickname used by fans and rivals to refer to the Russian football club Spartak Moscow, reflecting its historical association with the meat industry.
  • E. Yunaska
    Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
  • 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_69ab49e279e88190ab10d7248aea9d11 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd82f2020819086bbd321a750ce43 completed March 7, 2026, 7:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17aea398819092cb14b93abd0ff7 completed March 9, 2026, 6:55 p.m.
NEDg Description generation batch_69af18f9af388190bbb4242c89d4272e completed March 9, 2026, 7:01 p.m.
NED2 Entity disambiguation (via description) batch_69af198534c0819090c742f39501fac6 completed March 9, 2026, 7:03 p.m.
Created at: March 6, 2026, 9:45 p.m.