Triple

T3923568
Position Surface form Disambiguated ID Type / Status
Subject Šar Mountains E93217 entity
Predicate hasPeak P8205 FINISHED
Object Maja e Lubotenit
Maja e Lubotenit is a prominent peak in the Šar Mountains on the border of Kosovo and North Macedonia, known for its scenic alpine landscapes and popular hiking routes.
 E400311 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: Maja e Lubotenit | Statement: [Šar Mountains, hasPeak, Maja e Lubotenit]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Maja e Lubotenit
Context triple: [Šar Mountains, hasPeak, Maja e Lubotenit]
  • A. Romsa
    Romsa is the Northern Sami name for Tromsø, a major city in northern Norway known as a cultural and economic hub above the Arctic Circle.
  • B. Cimla
    Cimla is a residential suburb and community situated near the town of Neath in Neath Port Talbot, South Wales.
  • C. Kamenitsa
    Kamenitsa is a prominent mountain peak in Bulgaria’s Pirin range, known for its rugged alpine terrain and scenic hiking routes.
  • D. Lujza
    Lujza is a given name, primarily used in Central and Eastern Europe, that corresponds to the name Luisa or Louise in other languages.
  • E. Sagarejo
    Sagarejo is a town in eastern Georgia that serves as an important local center in the Kakheti wine-producing region.
  • 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: Maja e Lubotenit
Triple: [Šar Mountains, hasPeak, Maja e Lubotenit]
Generated description
Maja e Lubotenit is a prominent peak in the Šar Mountains on the border of Kosovo and North Macedonia, known for its scenic alpine landscapes and popular hiking routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Maja e Lubotenit
Target entity description: Maja e Lubotenit is a prominent peak in the Šar Mountains on the border of Kosovo and North Macedonia, known for its scenic alpine landscapes and popular hiking routes.
  • A. Romsa
    Romsa is the Northern Sami name for Tromsø, a major city in northern Norway known as a cultural and economic hub above the Arctic Circle.
  • B. Cimla
    Cimla is a residential suburb and community situated near the town of Neath in Neath Port Talbot, South Wales.
  • C. Kamenitsa
    Kamenitsa is a prominent mountain peak in Bulgaria’s Pirin range, known for its rugged alpine terrain and scenic hiking routes.
  • D. Lujza
    Lujza is a given name, primarily used in Central and Eastern Europe, that corresponds to the name Luisa or Louise in other languages.
  • E. Sagarejo
    Sagarejo is a town in eastern Georgia that serves as an important local center in the Kakheti wine-producing region.
  • 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_69aed96bfa1081908f7b30f2c647dee6 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeed7c2c848190a6d62e2df9b942d4 completed March 9, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b52870268881908463e30c11cda797 completed March 14, 2026, 9:20 a.m.
NEDg Description generation batch_69b52c3d0c0481909e869cf88e3f8a9d completed March 14, 2026, 9:37 a.m.
NED2 Entity disambiguation (via description) batch_69b52cb30f5c819095ac6e101a14b880 completed March 14, 2026, 9:38 a.m.
Created at: March 9, 2026, 3:23 p.m.