Triple

T7277657
Position Surface form Disambiguated ID Type / Status
Subject Cape Emine E163069 entity
Predicate nearSettlement P3883 FINISHED
Object Obzor
Obzor is a small Bulgarian Black Sea resort town known for its beaches and proximity to Cape Emine, the eastern end of the Balkan Mountains.
 E653870 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: Obzor | Statement: [Cape Emine, nearSettlement, Obzor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Obzor
Context triple: [Cape Emine, nearSettlement, Obzor]
  • A. Crnica
    Crnica is a river in central Serbia that flows through the city of Paraćin before joining the Velika Morava.
  • B. Ocuvite
    Ocuvite is a line of eye health dietary supplements formulated to support and protect vision, particularly in aging adults.
  • C. Sukošan
    Sukošan is a coastal village and popular tourist destination on the Adriatic Sea in Croatia, known for its marina and beaches near the city of Zadar.
  • D. Sretenje
    Sretenje is a Serbian national holiday commemorating both the country's first constitution and the beginning of its struggle for independence from the Ottoman Empire.
  • E. Vijećnica
    Vijećnica is the historic neo-Moorish landmark in Sarajevo that served as the city hall and later as the National and University Library of Bosnia and Herzegovina.
  • 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: Obzor
Triple: [Cape Emine, nearSettlement, Obzor]
Generated description
Obzor is a small Bulgarian Black Sea resort town known for its beaches and proximity to Cape Emine, the eastern end of the Balkan Mountains.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Obzor
Target entity description: Obzor is a small Bulgarian Black Sea resort town known for its beaches and proximity to Cape Emine, the eastern end of the Balkan Mountains.
  • A. Crnica
    Crnica is a river in central Serbia that flows through the city of Paraćin before joining the Velika Morava.
  • B. Ocuvite
    Ocuvite is a line of eye health dietary supplements formulated to support and protect vision, particularly in aging adults.
  • C. Sukošan
    Sukošan is a coastal village and popular tourist destination on the Adriatic Sea in Croatia, known for its marina and beaches near the city of Zadar.
  • D. Sretenje
    Sretenje is a Serbian national holiday commemorating both the country's first constitution and the beginning of its struggle for independence from the Ottoman Empire.
  • E. Vijećnica
    Vijećnica is the historic neo-Moorish landmark in Sarajevo that served as the city hall and later as the National and University Library of Bosnia and Herzegovina.
  • 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_69c6885c5964819085b209701769877f completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6eb309a648190a2a2f2cca9ce2f56 completed March 27, 2026, 8:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7db3110688190bf52180ea159c91c completed March 28, 2026, 1:44 p.m.
NEDg Description generation batch_69c7dbf65fb08190ae8a9c4e57d42e97 completed March 28, 2026, 1:47 p.m.
NED2 Entity disambiguation (via description) batch_69c7dc6873a081908ea4e953430ec20b completed March 28, 2026, 1:49 p.m.
Created at: March 27, 2026, 2:59 p.m.