Triple

T10056498
Position Surface form Disambiguated ID Type / Status
Subject Roman Spain E208874 entity
Predicate majorCity P316 FINISHED
Object Toletum
Toletum was an important ancient Roman city in central Hispania that later evolved into the modern city of Toledo in Spain.
 E838624 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: Toletum | Statement: [Roman Spain, majorCity, Toletum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Toletum
Context triple: [Roman Spain, majorCity, Toletum]
  • A. Tosali
    Tosali was an ancient city that served as a major political and administrative center of the Kalinga kingdom in eastern India.
  • B. Statilia
    Statilia is an ancient Roman feminine praenomen (given name) most notably borne by the empress Statilia Messalina, wife of Emperor Nero.
  • C. Sabinum
    Sabinum was the ancient central Italian region traditionally associated with the Sabine people, located in the Apennine area northeast of Rome.
  • D. Maenza
    Maenza is a small historic town in the Lazio region of central Italy, known for its medieval architecture and hilltop setting.
  • E. Laurentum
    Laurentum was an ancient coastal city in Latium, traditionally associated with the legendary king Latinus and early Roman mythic history.
  • 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: Toletum
Triple: [Roman Spain, majorCity, Toletum]
Generated description
Toletum was an important ancient Roman city in central Hispania that later evolved into the modern city of Toledo in Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Toletum
Target entity description: Toletum was an important ancient Roman city in central Hispania that later evolved into the modern city of Toledo in Spain.
  • A. Tosali
    Tosali was an ancient city that served as a major political and administrative center of the Kalinga kingdom in eastern India.
  • B. Statilia
    Statilia is an ancient Roman feminine praenomen (given name) most notably borne by the empress Statilia Messalina, wife of Emperor Nero.
  • C. Sabinum
    Sabinum was the ancient central Italian region traditionally associated with the Sabine people, located in the Apennine area northeast of Rome.
  • D. Maenza
    Maenza is a small historic town in the Lazio region of central Italy, known for its medieval architecture and hilltop setting.
  • E. Laurentum
    Laurentum was an ancient coastal city in Latium, traditionally associated with the legendary king Latinus and early Roman mythic history.
  • 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_69ca836094408190a36a1ea7e9a86fcd completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcfae503881909b9f016da4e2207d completed April 2, 2026, 2:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29a5258788190a4ecdefa5b520609 completed April 5, 2026, 5:22 p.m.
NEDg Description generation batch_69d29b7430248190b8965eaf1286dd7c completed April 5, 2026, 5:27 p.m.
NED2 Entity disambiguation (via description) batch_69d29c7ba9f081908f4614098d6c954b completed April 5, 2026, 5:31 p.m.
Created at: March 30, 2026, 8:57 p.m.