Triple

T16060093
Position Surface form Disambiguated ID Type / Status
Subject Móra d’Ebre E389584 entity
Predicate borderedBy P224 FINISHED
Object Móra la Nova
Móra la Nova is a municipality in Catalonia, Spain, situated on the right bank of the Ebro River in the Ribera d'Ebre comarca.
E1191079 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: Móra la Nova | Statement: [Móra d’Ebre, borderedBy, Móra la Nova]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Móra la Nova
Context triple: [Móra d’Ebre, borderedBy, Móra la Nova]
  • A. Maiao
    Maiao is a small, sparsely populated island in French Polynesia’s Society Islands, known for its traditional Polynesian lifestyle and limited accessibility.
  • 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. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • D. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • E. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • 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: Móra la Nova
Triple: [Móra d’Ebre, borderedBy, Móra la Nova]
Generated description
Móra la Nova is a municipality in Catalonia, Spain, situated on the right bank of the Ebro River in the Ribera d'Ebre comarca.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Móra la Nova
Target entity description: Móra la Nova is a municipality in Catalonia, Spain, situated on the right bank of the Ebro River in the Ribera d'Ebre comarca.
  • A. Maiao
    Maiao is a small, sparsely populated island in French Polynesia’s Society Islands, known for its traditional Polynesian lifestyle and limited accessibility.
  • 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. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • D. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • E. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • 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_69d86dae698881908327ef2d67706cb9 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1837850288190910ef37d6484c600 completed April 17, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbe88a608190bc0a0cbfdb71e81d completed May 10, 2026, 1:14 a.m.
NEDg Description generation batch_69ffdce9591c81909e6bb5c13ddf84cd completed May 10, 2026, 1:18 a.m.
NED2 Entity disambiguation (via description) batch_69ffddb0ff848190ace70b55d9861040 completed May 10, 2026, 1:21 a.m.
Created at: April 10, 2026, 4:57 a.m.