Triple

T5349107
Position Surface form Disambiguated ID Type / Status
Subject Lauro E124129 entity
Predicate hasCapital P204 FINISHED
Object Lauro
Lauro is a municipality that serves as its own administrative center, indicating that the town and its governing seat share the same name.
 E513705 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: Lauro | Statement: [Lauro, hasCapital, Lauro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lauro
Context triple: [Lauro, hasCapital, Lauro]
  • A. Lapa
    Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
  • B. Palmeira
    Palmeira is a coastal town on the island of Sal in Cape Verde, known for its fishing harbor and role as a local transport and trade hub.
  • C. Ciluba
    Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
  • D. Marulanda
    Marulanda is a small municipality and town located in the Caldas Department of Colombia, known for its rural Andean landscapes and agricultural economy.
  • E. La Ceiba
    La Ceiba is a prominent coastal city in northern Honduras known for its Caribbean port, vibrant nightlife, and annual carnival celebrations.
  • 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: Lauro
Triple: [Lauro, hasCapital, Lauro]
Generated description
Lauro is a municipality that serves as its own administrative center, indicating that the town and its governing seat share the same name.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lauro
Target entity description: Lauro is a municipality that serves as its own administrative center, indicating that the town and its governing seat share the same name.
  • A. Lapa
    Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
  • B. Palmeira
    Palmeira is a coastal town on the island of Sal in Cape Verde, known for its fishing harbor and role as a local transport and trade hub.
  • C. Ciluba
    Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
  • D. Marulanda
    Marulanda is a small municipality and town located in the Caldas Department of Colombia, known for its rural Andean landscapes and agricultural economy.
  • E. La Ceiba
    La Ceiba is a prominent coastal city in northern Honduras known for its Caribbean port, vibrant nightlife, and annual carnival celebrations.
  • 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_69bd464be27081908807b40b75c1bbae completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd860ea7088190ad7be14132927d17 completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf21d0d4d08190a33c86553d2012fa completed March 21, 2026, 10:55 p.m.
NEDg Description generation batch_69bf232d7a888190878f7a3ce769dd83 completed March 21, 2026, 11:01 p.m.
NED2 Entity disambiguation (via description) batch_69bf23c3ad9c8190b84a31a4b8fe8fca completed March 21, 2026, 11:03 p.m.
Created at: March 20, 2026, 2:01 p.m.