Triple

T3625794
Position Surface form Disambiguated ID Type / Status
Subject Fernando Primo de Rivera E76835 entity
Predicate givenName P17 FINISHED
Object Fernando
Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
E407735 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: Fernando | Statement: [Fernando Primo de Rivera, givenName, Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fernando
Context triple: [Fernando Primo de Rivera, givenName, Fernando]
  • A. Fernando
    Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
  • B. Fernando
    "Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
  • C. Fernando
    Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
  • D. Alfonso
    Alfonso is a masculine given name of Spanish and Italian origin historically borne by numerous kings, nobles, and notable figures across Europe.
  • E. Francisco
    Francisco is a masculine given name of Spanish and Portuguese origin, equivalent to Francis in English.
  • 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: Fernando
Triple: [Fernando Primo de Rivera, givenName, Fernando]
Generated description
Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Fernando
Target entity description: Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
  • A. Fernando
    Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
  • B. Fernando
    "Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
  • C. Fernando
    Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
  • D. Alfonso
    Alfonso is a masculine given name of Spanish and Italian origin historically borne by numerous kings, nobles, and notable figures across Europe.
  • E. Francisco
    Francisco is a masculine given name of Spanish and Portuguese origin, equivalent to Francis in English.
  • 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_69ad85dc03948190b35b7189e4175bcc completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc2dae7a48190809d4455b2349aaa completed March 8, 2026, 6:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69b556076b34819097b15b60a91b5164 completed March 14, 2026, 12:35 p.m.
NEDg Description generation batch_69b556efc2148190a66e732e09e4fc21 completed March 14, 2026, 12:39 p.m.
NED2 Entity disambiguation (via description) batch_69b5575de0e88190843f1fcbc12c9dd5 completed March 14, 2026, 12:41 p.m.
Created at: March 8, 2026, 3:23 p.m.