Triple

T2047535
Position Surface form Disambiguated ID Type / Status
Subject Bruttium E45487 entity
Predicate importantCity P3940 FINISHED
Object Terina
Terina was an ancient Greek-founded city in southern Italy’s Bruttium region, known as a significant coastal and commercial center in Magna Graecia.
 E228219 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: Terina | Statement: [Bruttium, importantCity, Terina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Terina
Context triple: [Bruttium, importantCity, Terina]
  • A. Tianeti
    Tianeti is a small town and administrative center in eastern Georgia, situated in the mountainous Mtskheta-Mtianeti region.
  • B. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • C. Mella
    Mella is a Spanish-language surname most notably associated with Cuban revolutionary leader Julio Antonio Mella.
  • D. Silvana
    Silvana is a feminine given name used in various cultures, often associated with meanings related to forests or woods.
  • E. Tara
    Tara is a female given name commonly used in English-speaking countries, often associated with notable figures such as Olympic figure skater Tara Lipinski.
  • 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: Terina
Triple: [Bruttium, importantCity, Terina]
Generated description
Terina was an ancient Greek-founded city in southern Italy’s Bruttium region, known as a significant coastal and commercial center in Magna Graecia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Terina
Target entity description: Terina was an ancient Greek-founded city in southern Italy’s Bruttium region, known as a significant coastal and commercial center in Magna Graecia.
  • A. Tianeti
    Tianeti is a small town and administrative center in eastern Georgia, situated in the mountainous Mtskheta-Mtianeti region.
  • B. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • C. Mella
    Mella is a Spanish-language surname most notably associated with Cuban revolutionary leader Julio Antonio Mella.
  • D. Silvana
    Silvana is a feminine given name used in various cultures, often associated with meanings related to forests or woods.
  • E. Tara
    Tara is a female given name commonly used in English-speaking countries, often associated with notable figures such as Olympic figure skater Tara Lipinski.
  • 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_69a8891948208190ab7898da21824c77 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb974e8488190887b840c2cb88b3a completed March 7, 2026, 5:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2003e9488190b54ff042c91d4a62 completed March 9, 2026, 1:19 a.m.
NEDg Description generation batch_69ae209544d881909438630ca5970d84 completed March 9, 2026, 1:21 a.m.
NED2 Entity disambiguation (via description) batch_69ae210b7b888190a879effa9d1e6ac3 completed March 9, 2026, 1:23 a.m.
Created at: March 4, 2026, 7:39 p.m.