Triple

T7861560
Position Surface form Disambiguated ID Type / Status
Subject Scuola Normale Superiore di Pisa E182510 entity
Predicate abbreviation P43 FINISHED
Object SNS
SNS is the acronym for the Scuola Normale Superiore di Pisa, an elite Italian higher education and research institution renowned for its rigorous academic standards.
 E695831 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: SNS | Statement: [Scuola Normale Superiore di Pisa, abbreviation, SNS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SNS
Context triple: [Scuola Normale Superiore di Pisa, abbreviation, SNS]
  • A. SNS
    SNS is the School of Natural Sciences at the University of California, Merced, which focuses on education and research in scientific disciplines such as biology, chemistry, physics, and related fields.
  • B. SNS
    SNS is the National Rail station code for Staines railway station in Surrey, England.
  • C. SNA
    SNA is the IATA airport code for John Wayne Airport, a commercial and general aviation airport serving Orange County, California.
  • D. SNA
    SNA is the commonly used abbreviation for the United Nations System of National Accounts, the international standard framework for measuring a country’s economic activity.
  • E. sns
    sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
  • 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: SNS
Triple: [Scuola Normale Superiore di Pisa, abbreviation, SNS]
Generated description
SNS is the acronym for the Scuola Normale Superiore di Pisa, an elite Italian higher education and research institution renowned for its rigorous academic standards.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SNS
Target entity description: SNS is the acronym for the Scuola Normale Superiore di Pisa, an elite Italian higher education and research institution renowned for its rigorous academic standards.
  • A. SNS
    SNS is the National Rail station code for Staines railway station in Surrey, England.
  • B. SNS
    SNS is the School of Natural Sciences at the University of California, Merced, which focuses on education and research in scientific disciplines such as biology, chemistry, physics, and related fields.
  • C. SNA
    SNA is the IATA airport code for John Wayne Airport, a commercial and general aviation airport serving Orange County, California.
  • D. SNA
    SNA is the commonly used abbreviation for the United Nations System of National Accounts, the international standard framework for measuring a country’s economic activity.
  • E. sns
    sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
  • 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_69ca82887fd48190975896bf38c4596b completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb36bcb5cc8190a8a384ce0f020b9f completed March 31, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b4138d081908a5ff16b79f0a0c8 completed March 31, 2026, 5:27 a.m.
NEDg Description generation batch_69cb5f1c9ef08190b1b79482f39966c7 completed March 31, 2026, 5:43 a.m.
NED2 Entity disambiguation (via description) batch_69cb767b198481909cfc1f7a44e6f0d8 completed March 31, 2026, 7:23 a.m.
Created at: March 30, 2026, 4:53 p.m.