Triple

T2890091
Position Surface form Disambiguated ID Type / Status
Subject Schuylkill Action Network E63797 entity
Predicate abbreviation P43 FINISHED
Object SAN
SAN is the acronym for the Schuylkill Action Network, a collaborative partnership focused on protecting and restoring the Schuylkill River watershed.
 E308612 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 | Statement: [Schuylkill Action Network, abbreviation, SAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SAN
Context triple: [Schuylkill Action Network, abbreviation, SAN]
  • A. SAN
    SAN is the three-letter IATA airport code for San Diego International Airport, the primary commercial airport serving the San Diego, California area.
  • B. SAM
    SAM is an analytical laboratory aboard NASA's Curiosity rover that studies Martian rocks, soil, and atmosphere to determine their chemical and organic composition.
  • C. SA
    SA is a UK postcode area covering Swansea and surrounding parts of South West Wales.
  • D. SA
    SA is a key 3GPP technical specification group responsible for defining the overall system architecture and service capabilities of mobile telecommunications networks.
  • E. SA
    SA is the standard abbreviation for South Australia, a state in the southern central part of Australia known for its wine regions, festivals, and coastal landscapes.
  • 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
Triple: [Schuylkill Action Network, abbreviation, SAN]
Generated description
SAN is the acronym for the Schuylkill Action Network, a collaborative partnership focused on protecting and restoring the Schuylkill River watershed.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SAN
Target entity description: SAN is the acronym for the Schuylkill Action Network, a collaborative partnership focused on protecting and restoring the Schuylkill River watershed.
  • A. SAN
    SAN is the three-letter IATA airport code for San Diego International Airport, the primary commercial airport serving the San Diego, California area.
  • B. SAM
    SAM is an analytical laboratory aboard NASA's Curiosity rover that studies Martian rocks, soil, and atmosphere to determine their chemical and organic composition.
  • C. SA
    SA is a UK postcode area covering Swansea and surrounding parts of South West Wales.
  • D. SA
    SA is a key 3GPP technical specification group responsible for defining the overall system architecture and service capabilities of mobile telecommunications networks.
  • E. SA
    SA is the standard abbreviation for South Australia, a state in the southern central part of Australia known for its wine regions, festivals, and coastal landscapes.
  • 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_69ab4c45822c8190830c5f2bb97bcfd0 completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe04a68ac8190aaeafe52138beb74 completed March 7, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b03179d7448190bcdbea164856aaa2 completed March 10, 2026, 2:58 p.m.
NEDg Description generation batch_69b03f0c5bac81909aa21d5963a86c92 completed March 10, 2026, 3:55 p.m.
NED2 Entity disambiguation (via description) batch_69b044c1ea3c8190a9ae7c1431d3a3f2 completed March 10, 2026, 4:20 p.m.
Created at: March 6, 2026, 10:07 p.m.