Triple

T16254086
Position Surface form Disambiguated ID Type / Status
Subject Las Vegas Altas del Guadiana E394584 entity
Predicate containsSettlement P847 FINISHED
Object La Haba
La Haba is a small municipality in the province of Badajoz, in the Extremadura region of western Spain.
 E1203039 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: La Haba | Statement: [Las Vegas Altas del Guadiana, containsSettlement, La Haba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: La Haba
Context triple: [Las Vegas Altas del Guadiana, containsSettlement, La Haba]
  • A. Hosaena
    Hosaena is a town in southern Ethiopia that serves as an important administrative and commercial center in the Southern Nations, Nationalities, and Peoples' Region.
  • B. El Basatin
    El Basatin is a district in the southern part of Cairo, Egypt, known primarily as a residential area within the Cairo Governorate.
  • C. Marquitos
    Marquitos is a Spanish diminutive form of the given name Marcos, often used as an affectionate nickname.
  • D. Pajalato
    Pajalato is an indigenous language spoken in parts of Mexico, known primarily from limited linguistic documentation and also referred to as Pajalate.
  • E. Les Muma
    Les Muma is an American businessman and philanthropist best known for his major contributions to the University of South Florida, where the business school bears his name.
  • 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: La Haba
Triple: [Las Vegas Altas del Guadiana, containsSettlement, La Haba]
Generated description
La Haba is a small municipality in the province of Badajoz, in the Extremadura region of western Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: La Haba
Target entity description: La Haba is a small municipality in the province of Badajoz, in the Extremadura region of western Spain.
  • A. Hosaena
    Hosaena is a town in southern Ethiopia that serves as an important administrative and commercial center in the Southern Nations, Nationalities, and Peoples' Region.
  • B. El Basatin
    El Basatin is a district in the southern part of Cairo, Egypt, known primarily as a residential area within the Cairo Governorate.
  • C. Marquitos
    Marquitos is a Spanish diminutive form of the given name Marcos, often used as an affectionate nickname.
  • D. Pajalato
    Pajalato is an indigenous language spoken in parts of Mexico, known primarily from limited linguistic documentation and also referred to as Pajalate.
  • E. Les Muma
    Les Muma is an American businessman and philanthropist best known for his major contributions to the University of South Florida, where the business school bears his name.
  • 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_69d87f2171208190951025e526947816 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24598c9488190a92df7d8b1824724 completed April 17, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a000ee788f88190b16d267f1eee6d62 completed May 10, 2026, 4:51 a.m.
NEDg Description generation batch_6a00113900c88190bf7f56ca4b16a84c completed May 10, 2026, 5:01 a.m.
NED2 Entity disambiguation (via description) batch_6a0011d98f708190805c84d63ed79aaa completed May 10, 2026, 5:04 a.m.
Created at: April 10, 2026, 5:04 a.m.