Triple

T10343321
Position Surface form Disambiguated ID Type / Status
Subject Via Domitiana E243679 entity
Predicate connects P390 FINISHED
Object Sinuessa
Sinuessa was an ancient Roman coastal town in Campania, Italy, known for its strategic location along major roads and its nearby thermal baths.
 E858349 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: Sinuessa | Statement: [Via Domitiana, connects, Sinuessa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sinuessa
Context triple: [Via Domitiana, connects, Sinuessa]
  • A. Ein Siniya
    Ein Siniya is a small Palestinian village in the central West Bank, known for its rural character and proximity to the town of Birzeit.
  • B. Kusaila
    Kusaila was a 7th-century Berber Christian leader and military commander who led resistance against the early Muslim expansion in North Africa.
  • C. Toinette
    Toinette is the sharp-witted, outspoken maid in Molière’s comedy "Le Malade imaginaire," known for her clever schemes and satirical commentary on her hypochondriac master.
  • D. Sijilmasa
    Sijilmasa was a medieval Moroccan oasis city that flourished as a key commercial hub linking North Africa with sub-Saharan gold and trade networks.
  • E. Kulisusu
    Kulisusu is a town and administrative center located in the province of Southeast Sulawesi, Indonesia.
  • 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: Sinuessa
Triple: [Via Domitiana, connects, Sinuessa]
Generated description
Sinuessa was an ancient Roman coastal town in Campania, Italy, known for its strategic location along major roads and its nearby thermal baths.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sinuessa
Target entity description: Sinuessa was an ancient Roman coastal town in Campania, Italy, known for its strategic location along major roads and its nearby thermal baths.
  • A. Ein Siniya
    Ein Siniya is a small Palestinian village in the central West Bank, known for its rural character and proximity to the town of Birzeit.
  • B. Kusaila
    Kusaila was a 7th-century Berber Christian leader and military commander who led resistance against the early Muslim expansion in North Africa.
  • C. Toinette
    Toinette is the sharp-witted, outspoken maid in Molière’s comedy "Le Malade imaginaire," known for her clever schemes and satirical commentary on her hypochondriac master.
  • D. Sijilmasa
    Sijilmasa was a medieval Moroccan oasis city that flourished as a key commercial hub linking North Africa with sub-Saharan gold and trade networks.
  • E. Kulisusu
    Kulisusu is a town and administrative center located in the province of Southeast Sulawesi, Indonesia.
  • 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_69d381b22b8c8190aaed476be5f872a9 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e92105888190a08104deb9d0cf1c completed April 7, 2026, 11:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69d75077dfbc81908de29aac1a3bb19f completed April 9, 2026, 7:08 a.m.
NEDg Description generation batch_69d7618c9abc819080c4d6669dfb8320 completed April 9, 2026, 8:21 a.m.
NED2 Entity disambiguation (via description) batch_69d7702ae24481908b0f5319413e81d4 completed April 9, 2026, 9:23 a.m.
Created at: April 6, 2026, 11:55 a.m.