Triple

T14458821
Position Surface form Disambiguated ID Type / Status
Subject Valentano E358528 entity
Predicate hasLandmark P105 FINISHED
Object Porta Romana
Porta Romana is a historic town gate in Valentano, Italy, notable as one of the main entrances to its old medieval center.
E1100355 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: Porta Romana | Statement: [Valentano, hasLandmark, Porta Romana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Porta Romana
Context triple: [Valentano, hasLandmark, Porta Romana]
  • A. Porta Romana
    Porta Romana is a historic city gate in Viterbo, Italy, serving as one of the traditional entrances through the town’s medieval walls.
  • B. Porta Romana
    Porta Romana is a historic city gate in Norcia, Italy, notable as one of the main entrances through the town’s medieval walls.
  • C. Porta Romana
    Porta Romana is a historic city gate in Velletri, Italy, notable as one of the traditional entrances to the town.
  • D. Porta Romana
    Porta Romana is a historic city gate of Terra del Sole in Italy, notable as one of the main fortified entrances to the Renaissance-planned town.
  • E. Porta Roma
    Porta Roma is an ancient city gate in Santa Maria Capua Vetere, Italy, historically serving as a main entrance along the route toward Rome.
  • 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: Porta Romana
Triple: [Valentano, hasLandmark, Porta Romana]
Generated description
Porta Romana is a historic town gate in Valentano, Italy, notable as one of the main entrances to its old medieval center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Porta Romana
Target entity description: Porta Romana is a historic town gate in Valentano, Italy, notable as one of the main entrances to its old medieval center.
  • A. Porta Romana
    Porta Romana is a historic city gate in Viterbo, Italy, serving as one of the traditional entrances through the town’s medieval walls.
  • B. Porta Romana
    Porta Romana is a historic city gate in Velletri, Italy, notable as one of the traditional entrances to the town.
  • C. Porta Romana
    Porta Romana is a historic city gate in Norcia, Italy, notable as one of the main entrances through the town’s medieval walls.
  • D. Porta Romana
    Porta Romana is a historic city gate of Terra del Sole in Italy, notable as one of the main fortified entrances to the Renaissance-planned town.
  • E. Porta Roma
    Porta Roma is an ancient city gate in Santa Maria Capua Vetere, Italy, historically serving as a main entrance along the route toward Rome.
  • 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_69d82794dfa081909b9134ad2e32244b completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91aabebc819097eb61b2d81c9a91 completed April 14, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd64935d8081908e5b0e80027948e0 completed May 8, 2026, 4:20 a.m.
NEDg Description generation batch_69fd65cf06308190bf7b6463bc109542 completed May 8, 2026, 4:25 a.m.
NED2 Entity disambiguation (via description) batch_69fd66476ab88190b2d410ced33ce34b completed May 8, 2026, 4:27 a.m.
Created at: April 10, 2026, 1:19 a.m.