Triple

T10183841
Position Surface form Disambiguated ID Type / Status
Subject Port of Chornomorsk E236857 entity
Predicate locatedIn P40 FINISHED
Object Chornomorsk
Chornomorsk is a Ukrainian port city on the Black Sea known for its major maritime and cargo-handling facilities.
 E236857 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: Chornomorsk | Statement: [Port of Chornomorsk, locatedIn, Chornomorsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Chornomorsk
Context triple: [Port of Chornomorsk, locatedIn, Chornomorsk]
  • A. Odesa
    Odesa is a major port city on the Black Sea in southern Ukraine, known for its historic architecture, multicultural heritage, and key economic and cultural role in the country.
  • B. Mykolaiv
    Mykolaiv is a major shipbuilding and industrial city in southern Ukraine located near the Black Sea.
  • C. Kryvyi Rih
    Kryvyi Rih is a major industrial city in central Ukraine known for its extensive iron ore mining and steel production.
  • D. Port of Chornomorsk
    The Port of Chornomorsk is a major Ukrainian Black Sea seaport and transport hub near Odesa, handling significant cargo and passenger traffic.
  • E. Sievierodonetsk
    Sievierodonetsk is an industrial city in eastern Ukraine that became a focal point of intense fighting during the war in the Donbas and the 2022 Russian invasion.
  • 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: Chornomorsk
Triple: [Port of Chornomorsk, locatedIn, Chornomorsk]
Generated description
Chornomorsk is a Ukrainian port city on the Black Sea known for its major maritime and cargo-handling facilities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Chornomorsk
Target entity description: Chornomorsk is a Ukrainian port city on the Black Sea known for its major maritime and cargo-handling facilities.
  • A. Odesa
    Odesa is a major port city on the Black Sea in southern Ukraine, known for its historic architecture, multicultural heritage, and key economic and cultural role in the country.
  • B. Mykolaiv
    Mykolaiv is a major shipbuilding and industrial city in southern Ukraine located near the Black Sea.
  • C. Kryvyi Rih
    Kryvyi Rih is a major industrial city in central Ukraine known for its extensive iron ore mining and steel production.
  • D. Port of Chornomorsk chosen
    The Port of Chornomorsk is a major Ukrainian Black Sea seaport and transport hub near Odesa, handling significant cargo and passenger traffic.
  • E. Sievierodonetsk
    Sievierodonetsk is an industrial city in eastern Ukraine that became a focal point of intense fighting during the war in the Donbas and the 2022 Russian invasion.
  • F. None of above.

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_69ca84d7260c8190bfbec36762943f37 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cded3566f881909e0d1366f501d554 completed April 2, 2026, 4:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71c68d0cc8190994c4b0aaaf7829a completed April 9, 2026, 3:26 a.m.
NEDg Description generation batch_69d73180d90481908f1b4768230edd36 completed April 9, 2026, 4:56 a.m.
NED2 Entity disambiguation (via description) batch_69d7326b14988190bff33dc01e690707 completed April 9, 2026, 5 a.m.
Created at: March 30, 2026, 9:12 p.m.