Triple

T3173776
Position Surface form Disambiguated ID Type / Status
Subject Stored Communications Act E66413 entity
Predicate shortName P43 FINISHED
Object SCA
SCA is a U.S. federal law that governs the privacy and disclosure of stored electronic communications and related data held by service providers.
 E333097 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: SCA | Statement: [Stored Communications Act, shortName, SCA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SCA
Context triple: [Stored Communications Act, shortName, SCA]
  • A. SCA
    SCA is the National Rail station code for Scarborough railway station in North Yorkshire, England.
  • B. SCAR
    SCAR is an international scientific body that coordinates and promotes research in and about Antarctica and the Southern Ocean.
  • C. SCMA
    SCMA is the Smith College Museum of Art, a prominent academic art museum known for its diverse collections and educational programs in Northampton, Massachusetts.
  • D. SCS
    SCS is Carnegie Mellon University's renowned School of Computer Science, recognized globally for pioneering research and education in computing and related fields.
  • E. SICA
    SICA is a regional organization that promotes political, economic, and social integration among Central American countries.
  • 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: SCA
Triple: [Stored Communications Act, shortName, SCA]
Generated description
SCA is a U.S. federal law that governs the privacy and disclosure of stored electronic communications and related data held by service providers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SCA
Target entity description: SCA is a U.S. federal law that governs the privacy and disclosure of stored electronic communications and related data held by service providers.
  • A. SCA
    SCA is the National Rail station code for Scarborough railway station in North Yorkshire, England.
  • B. SCAR
    SCAR is an international scientific body that coordinates and promotes research in and about Antarctica and the Southern Ocean.
  • C. SCMA
    SCMA is the Smith College Museum of Art, a prominent academic art museum known for its diverse collections and educational programs in Northampton, Massachusetts.
  • D. SCS
    SCS is Carnegie Mellon University's renowned School of Computer Science, recognized globally for pioneering research and education in computing and related fields.
  • E. SICA
    SICA is a regional organization that promotes political, economic, and social integration among Central American countries.
  • 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_69ad8586a34c8190944c63ec11a8de1a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada66facf881908b9ec687d68ce91b completed March 8, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69b235edf7708190b79605a05baf1711 completed March 12, 2026, 3:41 a.m.
NEDg Description generation batch_69b236e61ae88190a76b942c6cddff41 completed March 12, 2026, 3:45 a.m.
NED2 Entity disambiguation (via description) batch_69b23770ed4c8190b5d929cc95a286a0 completed March 12, 2026, 3:48 a.m.
Created at: March 8, 2026, 3:06 p.m.