Triple

T14037751
Position Surface form Disambiguated ID Type / Status
Subject Luanda Province E337756 entity
Predicate hasCity P316 FINISHED
Object Belas
Belas is a rapidly developing urban municipality in Luanda Province, Angola, known for its residential expansion and emerging commercial infrastructure.
 E1075084 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: Belas | Statement: [Luanda Province, hasCity, Belas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Belas
Context triple: [Luanda Province, hasCity, Belas]
  • A. Belasí
    Belasí is the Slovak nickname for HC Slovan Bratislava, referring to the club’s traditional light blue team color.
  • B. Djauro
    Djauro is an alternative name for the Yawuru, an Aboriginal Australian people traditionally associated with the Broome region of Western Australia.
  • C. Bourkika
    Bourkika is a town and commune in northern Algeria, situated within the coastal Tipaza Province west of Algiers.
  • D. Cameia
    Cameia is a small town in eastern Angola’s Moxico Province, known as a gateway to the nearby Cameia National Park.
  • E. Kamorta
    Kamorta is a significant inhabited island and settlement in India’s Nicobar archipelago, known for its strategic location and indigenous Nicobarese communities.
  • 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: Belas
Triple: [Luanda Province, hasCity, Belas]
Generated description
Belas is a rapidly developing urban municipality in Luanda Province, Angola, known for its residential expansion and emerging commercial infrastructure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Belas
Target entity description: Belas is a rapidly developing urban municipality in Luanda Province, Angola, known for its residential expansion and emerging commercial infrastructure.
  • A. Belasí
    Belasí is the Slovak nickname for HC Slovan Bratislava, referring to the club’s traditional light blue team color.
  • B. Djauro
    Djauro is an alternative name for the Yawuru, an Aboriginal Australian people traditionally associated with the Broome region of Western Australia.
  • C. Bourkika
    Bourkika is a town and commune in northern Algeria, situated within the coastal Tipaza Province west of Algiers.
  • D. Cameia
    Cameia is a small town in eastern Angola’s Moxico Province, known as a gateway to the nearby Cameia National Park.
  • E. Kamorta
    Kamorta is a significant inhabited island and settlement in India’s Nicobar archipelago, known for its strategic location and indigenous Nicobarese communities.
  • 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_69d81c664e48819088cbd8f433aeffe5 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de30e312148190a6be0a3258364e6e completed April 14, 2026, 12:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc33bc20081909abea7e64d1bd578 completed May 6, 2026, 10:39 p.m.
NEDg Description generation batch_69fbc53729d081908b74532d2ed54b7a completed May 6, 2026, 10:48 p.m.
NED2 Entity disambiguation (via description) batch_69fbc5d76cdc8190970778580437cf72 completed May 6, 2026, 10:51 p.m.
Created at: April 9, 2026, 10:20 p.m.