Triple

T9658954
Position Surface form Disambiguated ID Type / Status
Subject Lauro Cavazos E233537 entity
Predicate givenName P17 FINISHED
Object Lauro
Lauro is a masculine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries and derived from the word for "laurel."
 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 Cavazos, givenName, Lauro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lauro
Context triple: [Lauro Cavazos, givenName, Lauro]
  • A. Lauro
    Lauro is a municipality that serves as its own administrative center, indicating that the town and its governing seat share the same name.
  • B. Lapa
    Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
  • C. 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.
  • D. Ciluba
    Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
  • E. Marulanda
    Marulanda is a small municipality and town located in the Caldas Department of Colombia, known for its rural Andean landscapes and agricultural economy.
  • 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 Cavazos, givenName, Lauro]
Generated description
Lauro is a masculine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries and derived from the word for "laurel."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lauro
Target entity description: Lauro is a masculine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries and derived from the word for "laurel."
  • A. Lauro chosen
    Lauro is a municipality that serves as its own administrative center, indicating that the town and its governing seat share the same name.
  • B. Lapa
    Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
  • C. 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.
  • D. Ciluba
    Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
  • E. Marulanda
    Marulanda is a small municipality and town located in the Caldas Department of Colombia, known for its rural Andean landscapes and agricultural economy.
  • F. None of above.

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_69ca848c1ba88190b84b410cd14627fc completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9bdfc3b08190835e86ff99663214 completed April 1, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a0b84a0819083191beeaf8d968b completed April 4, 2026, 10 p.m.
NEDg Description generation batch_69d18ac0796c8190b48ccdb9c5052332 completed April 4, 2026, 10:03 p.m.
NED2 Entity disambiguation (via description) batch_69d18b7f7510819083a402d6802c7d95 completed April 4, 2026, 10:06 p.m.
Created at: March 30, 2026, 8:14 p.m.