Triple

T7995552
Position Surface form Disambiguated ID Type / Status
Subject Spello E186113 entity
Predicate hasChurch P15000 FINISHED
Object San Lorenzo
San Lorenzo is a historic church in the Italian town of Spello, known for its medieval architecture and religious significance.
E705314 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: San Lorenzo | Statement: [Spello, hasChurch, San Lorenzo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Lorenzo
Context triple: [Spello, hasChurch, San Lorenzo]
  • A. San Lorenzo
    San Lorenzo is a coastal municipality on the island province of Guimaras in the Philippines, known for its rural communities and agricultural landscape.
  • B. San Lorenzo
    San Lorenzo is an upscale commercial and residential district in Makati, Metro Manila, known for its gated villages, shopping centers, and proximity to the central business area.
  • C. San Lorenzo
    San Lorenzo is an unincorporated community in Alameda County, California, located in the East Bay region of the San Francisco Bay Area.
  • D. San Lorenzo
    San Lorenzo is a municipality in the central-eastern region of Puerto Rico known for its rural landscapes and small-town character.
  • E. San Lorenzo
    San Lorenzo is one of Argentina’s traditional “big five” football clubs, based in Buenos Aires and known for its passionate fan base and historic success in domestic and international competitions.
  • 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: San Lorenzo
Triple: [Spello, hasChurch, San Lorenzo]
Generated description
San Lorenzo is a historic church in the Italian town of Spello, known for its medieval architecture and religious significance.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: San Lorenzo
Target entity description: San Lorenzo is a historic church in the Italian town of Spello, known for its medieval architecture and religious significance.
  • A. San Lorenzo
    San Lorenzo is a municipality in the central-eastern region of Puerto Rico known for its rural landscapes and small-town character.
  • B. San Lorenzo
    San Lorenzo is a coastal municipality on the island province of Guimaras in the Philippines, known for its rural communities and agricultural landscape.
  • C. San Lorenzo
    San Lorenzo is an upscale commercial and residential district in Makati, Metro Manila, known for its gated villages, shopping centers, and proximity to the central business area.
  • D. San Lorenzo
    San Lorenzo is an unincorporated community in Alameda County, California, located in the East Bay region of the San Francisco Bay Area.
  • E. San Lorenzo
    San Lorenzo is one of Argentina’s traditional “big five” football clubs, based in Buenos Aires and known for its passionate fan base and historic success in domestic and international competitions.
  • 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_69ca829c6c308190ab05b43d234c52b2 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c7547dc8190a60a06b3d61764b0 completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe10d5eb081909f257390094de442 completed March 31, 2026, 2:58 p.m.
NEDg Description generation batch_69cc46c221848190848c7e017e532a16 completed March 31, 2026, 10:12 p.m.
NED2 Entity disambiguation (via description) batch_69cc480d2f40819085046a1d0c9d05e0 completed March 31, 2026, 10:17 p.m.
Created at: March 30, 2026, 5:17 p.m.