Triple

T2035575
Position Surface form Disambiguated ID Type / Status
Subject Porter Square Shopping Center E44617 entity
Predicate hasTenant P3277 FINISHED
Object Michael’s
Michael’s is a national arts and crafts retail chain known for selling hobby supplies, home décor, and DIY project materials.
 E227920 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: Michael’s | Statement: [Porter Square Shopping Center, hasTenant, Michael’s]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Michael’s
Context triple: [Porter Square Shopping Center, hasTenant, Michael’s]
  • A. Micheal
    Micheal is a given name, typically a variant spelling of the more common name Michael.
  • B. MIKE
    MIKE is a high-resolution optical spectrograph used on the Magellan Telescopes for detailed astronomical spectroscopy.
  • C. Michaël
    Michaël is a given name, typically a French or Dutch variant of the name Michael, used for males in various European countries.
  • D. Michele
    Michele is a given name used as a variant of Michael in various languages and cultures.
  • E. Mick
    Mick is the commonly used nickname of American politician and former White House Chief of Staff Mick Mulvaney.
  • 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: Michael’s
Triple: [Porter Square Shopping Center, hasTenant, Michael’s]
Generated description
Michael’s is a national arts and crafts retail chain known for selling hobby supplies, home décor, and DIY project materials.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Michael’s
Target entity description: Michael’s is a national arts and crafts retail chain known for selling hobby supplies, home décor, and DIY project materials.
  • A. Micheal
    Micheal is a given name, typically a variant spelling of the more common name Michael.
  • B. MIKE
    MIKE is a high-resolution optical spectrograph used on the Magellan Telescopes for detailed astronomical spectroscopy.
  • C. Michaël
    Michaël is a given name, typically a French or Dutch variant of the name Michael, used for males in various European countries.
  • D. Michele
    Michele is a given name used as a variant of Michael in various languages and cultures.
  • E. Mick
    Mick is the commonly used nickname of American politician and former White House Chief of Staff Mick Mulvaney.
  • 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_69a889159ec481908f9e4472d9f480c7 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb934ff948190acd88d4f587463a4 completed March 7, 2026, 5:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1ff03d3c8190adef0224aac989bd completed March 9, 2026, 1:18 a.m.
NEDg Description generation batch_69ae20946a288190a3bd2a19e3608e86 completed March 9, 2026, 1:21 a.m.
NED2 Entity disambiguation (via description) batch_69ae2109d17c819094a298a822064052 completed March 9, 2026, 1:23 a.m.
Created at: March 4, 2026, 7:39 p.m.