Triple

T15265454
Position Surface form Disambiguated ID Type / Status
Subject San Román Province E364888 entity
Predicate namedAfter P63 FINISHED
Object San Román
San Román is a namesake figure—likely a Christian saint—after whom the San Román Province is named.
 E570859 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: San Román | Statement: [San Román Province, namedAfter, San Román]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Román
Context triple: [San Román Province, namedAfter, San Román]
  • A. San Román
    San Román is a Spanish surname commonly borne by individuals of Hispanic origin.
  • B. San Marcelino
    San Marcelino is a landlocked municipality in the province of Zambales in the Philippines, known for its agricultural economy and proximity to the Subic Bay area.
  • C. San Pardo
    San Pardo is a Christian saint venerated as the patron of Larino in Italy, traditionally associated with protecting the town and honored through local religious festivals and processions.
  • D. San Marcial
    San Marcial is a hill near Irun in northern Spain, historically notable as the site of key battles during the Peninsular War.
  • E. San Martín de la Vega
    San Martín de la Vega is a municipality in the Community of Madrid, Spain, known for hosting the Parque Warner Madrid theme park.
  • 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: San Román
Triple: [San Román Province, namedAfter, San Román]
Generated description
San Román is a namesake figure—likely a Christian saint—after whom the San Román Province is named.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Román
Target entity description: San Román is a namesake figure—likely a Christian saint—after whom the San Román Province is named.
  • A. San Román chosen
    San Román is a Spanish surname commonly borne by individuals of Hispanic origin.
  • B. San Marcelino
    San Marcelino is a landlocked municipality in the province of Zambales in the Philippines, known for its agricultural economy and proximity to the Subic Bay area.
  • C. San Pardo
    San Pardo is a Christian saint venerated as the patron of Larino in Italy, traditionally associated with protecting the town and honored through local religious festivals and processions.
  • D. San Marcial
    San Marcial is a hill near Irun in northern Spain, historically notable as the site of key battles during the Peninsular War.
  • E. San Martín de la Vega
    San Martín de la Vega is a municipality in the Community of Madrid, Spain, known for hosting the Parque Warner Madrid theme park.
  • 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00851c5b88190a296b6a105d3ee30 completed April 15, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5fdc21881909d87062db6fb8fb7 completed May 9, 2026, 7:45 a.m.
NEDg Description generation batch_69fee714cf6c81908dc4427590eeae85 completed May 9, 2026, 7:49 a.m.
NED2 Entity disambiguation (via description) batch_69feeae4731081909964bd8b1ea3dd7a completed May 9, 2026, 8:05 a.m.
Created at: April 10, 2026, 3:14 a.m.