Triple

T8966855
Position Surface form Disambiguated ID Type / Status
Subject Risaralda Department E214157 entity
Predicate contains P35 FINISHED
Object Marsella
Marsella is a small Colombian town known for its traditional architecture and coffee-growing culture in the Andean region.
E770606 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: Marsella | Statement: [Risaralda Department, contains, Marsella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marsella
Context triple: [Risaralda Department, contains, Marsella]
  • A. Bononia
    Bononia is the ancient Roman name for the city of Bologna in northern Italy, an important urban and commercial center in the Emilia-Romagna region.
  • B. Bononia
    Bononia was an important Roman fortress and settlement along the Danube frontier in the province of Dacia Ripensis.
  • C. La Marsa
    La Marsa is a coastal suburb of Tunis in northern Tunisia, known for its beaches, upscale residential areas, and historic seaside charm.
  • D. Provenza
    "Provenza" is a hit reggaeton song by Colombian singer Karol G known for its tropical sound and themes of empowerment and romantic liberation.
  • E. Rivoli
    Rivoli is a town in the Piedmont region of northern Italy, near Turin, known for its historic castle and strategic location.
  • 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: Marsella
Triple: [Risaralda Department, contains, Marsella]
Generated description
Marsella is a small Colombian town known for its traditional architecture and coffee-growing culture in the Andean region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Marsella
Target entity description: Marsella is a small Colombian town known for its traditional architecture and coffee-growing culture in the Andean region.
  • A. Bononia
    Bononia is the ancient Roman name for the city of Bologna in northern Italy, an important urban and commercial center in the Emilia-Romagna region.
  • B. Bononia
    Bononia was an important Roman fortress and settlement along the Danube frontier in the province of Dacia Ripensis.
  • C. La Marsa
    La Marsa is a coastal suburb of Tunis in northern Tunisia, known for its beaches, upscale residential areas, and historic seaside charm.
  • D. Provenza
    "Provenza" is a hit reggaeton song by Colombian singer Karol G known for its tropical sound and themes of empowerment and romantic liberation.
  • E. Rivoli
    Rivoli is a town in the Piedmont region of northern Italy, near Turin, known for its historic castle and strategic location.
  • 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_69ca839cd6008190a1546a701a56710c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc676389948190aa78fdf6a5ae74a5 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0a753f8819084b952f20997c8d6 completed April 3, 2026, 2:37 p.m.
NEDg Description generation batch_69cfd17e5850819087fbb60fdc612fd9 completed April 3, 2026, 2:41 p.m.
NED2 Entity disambiguation (via description) batch_69cfd215204c8190886cc071f100aab6 completed April 3, 2026, 2:43 p.m.
Created at: March 30, 2026, 7:01 p.m.