Triple

T16033061
Position Surface form Disambiguated ID Type / Status
Subject Arsena of Marabda E388895 entity
Predicate settingPlace P1957 FINISHED
Object Marabda
Marabda is a village in Georgia known as the setting of the Georgian literary work "Arsena of Marabda."
E1190045 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: Marabda | Statement: [Arsena of Marabda, settingPlace, Marabda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marabda
Context triple: [Arsena of Marabda, settingPlace, Marabda]
  • A. Mararaba
    Mararaba is a rapidly growing suburban town near Abuja in central Nigeria, known for its dense population and heavy commuter traffic.
  • B. Laabi
    Laabi is a small village located in Harku Parish in northern Estonia.
  • C. Matabaan
    Matabaan is a town in central Somalia that serves as one of the urban centers within the federal member state of Hirshabelle.
  • D. Dschubba
    Dschubba is a bright multiple star system that forms the "head" of the constellation Scorpius and is designated Delta Scorpii.
  • E. Jableh
    Jableh is a coastal city in northwestern Syria on the Mediterranean Sea, known for its ancient history and archaeological sites, including a well-preserved Roman theater.
  • 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: Marabda
Triple: [Arsena of Marabda, settingPlace, Marabda]
Generated description
Marabda is a village in Georgia known as the setting of the Georgian literary work "Arsena of Marabda."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Marabda
Target entity description: Marabda is a village in Georgia known as the setting of the Georgian literary work "Arsena of Marabda."
  • A. Mararaba
    Mararaba is a rapidly growing suburban town near Abuja in central Nigeria, known for its dense population and heavy commuter traffic.
  • B. Laabi
    Laabi is a small village located in Harku Parish in northern Estonia.
  • C. Matabaan
    Matabaan is a town in central Somalia that serves as one of the urban centers within the federal member state of Hirshabelle.
  • D. Dschubba
    Dschubba is a bright multiple star system that forms the "head" of the constellation Scorpius and is designated Delta Scorpii.
  • E. Jableh
    Jableh is a coastal city in northwestern Syria on the Mediterranean Sea, known for its ancient history and archaeological sites, including a well-preserved Roman theater.
  • 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_69d86dada3808190825d5f80d72fbe88 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e183396a708190b53a589f6ac2c5bc completed April 17, 2026, 12:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf373f548190abfc93aa238b97fd completed May 10, 2026, 12:20 a.m.
NEDg Description generation batch_69ffd13281208190a0c882563031b0eb completed May 10, 2026, 12:28 a.m.
NED2 Entity disambiguation (via description) batch_69ffd1e3cf348190a75b6471e0c1a4d2 completed May 10, 2026, 12:31 a.m.
Created at: April 10, 2026, 4:56 a.m.