Triple

T3698857
Position Surface form Disambiguated ID Type / Status
Subject Évora District E78524 entity
Predicate contains P35 FINISHED
Object Mora
Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
 E382048 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: Mora | Statement: [Évora District, contains, Mora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mora
Context triple: [Évora District, contains, Mora]
  • A. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • B. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • C. Martos
    Martos is a historic town in southern Spain’s Andalusia region, known for its olive oil production and hilltop setting dominated by a medieval castle.
  • D. Odda
    Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
  • E. Morar
    Morar is a small coastal village in the Lochaber area of the Scottish Highlands, known for its scenic beaches and proximity to the Road to the Isles.
  • 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: Mora
Triple: [Évora District, contains, Mora]
Generated description
Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mora
Target entity description: Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • A. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • B. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • C. Martos
    Martos is a historic town in southern Spain’s Andalusia region, known for its olive oil production and hilltop setting dominated by a medieval castle.
  • D. Odda
    Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
  • E. Morar
    Morar is a small coastal village in the Lochaber area of the Scottish Highlands, known for its scenic beaches and proximity to the Road to the Isles.
  • 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_69ad85e3b1888190abc983e06968696d completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc512ba188190a15bcacafac3f476 completed March 8, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4cdf1b16081909b18af630d0b4817 completed March 14, 2026, 2:54 a.m.
NEDg Description generation batch_69b4d1a5411c81909f464f8abc012177 completed March 14, 2026, 3:10 a.m.
NED2 Entity disambiguation (via description) batch_69b4d20a7fa8819093e8e66ba9272f31 completed March 14, 2026, 3:12 a.m.
Created at: March 8, 2026, 3:26 p.m.