Triple

T27095572
Position Surface form Disambiguated ID Type / Status
Subject Roman Theatre of Volterra E686286 entity
Predicate discoveredBy P412 FINISHED
Object Enrico Fiumi
Enrico Fiumi was an Italian archaeologist and scholar best known for uncovering and studying the ancient Roman heritage of Volterra.
 E2295495 NE FINISHED

How this triple was built (2 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: Enrico Fiumi | Statement: [Roman Theatre of Volterra, discoveredBy, Enrico Fiumi]
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: Enrico Fiumi
Triple: [Roman Theatre of Volterra, discoveredBy, Enrico Fiumi]
Generated description
Enrico Fiumi was an Italian archaeologist and scholar best known for uncovering and studying the ancient Roman heritage of Volterra.

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_69ef1489f8b481908e24a1985982bd26 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f623b0875481909576d6809b3a5569 completed May 2, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7d61f889c48190b32facf284d73b86 completed Aug. 13, 2026, 6:19 a.m.
NEDg Description generation batch_6a7d624dc67481909d03c55e28ca6484 completed Aug. 13, 2026, 6:21 a.m.
NED2 Entity disambiguation (via description) batch_6a7d62ea80588190a782c4d64e52c8d2 completed Aug. 13, 2026, 6:23 a.m.
Created at: April 27, 2026, 8:44 a.m.