Triple

T1596667
Position Surface form Disambiguated ID Type / Status
Subject Saint Louis Art Museum E34296 entity
Predicate hasNameVariant P457 FINISHED
Object SLAM
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
 E181576 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: SLAM | Statement: [Saint Louis Art Museum, hasNameVariant, SLAM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SLAM
Context triple: [Saint Louis Art Museum, hasNameVariant, SLAM]
  • A. SLM
    SLM is the stock ticker symbol for Sanlam, a major South African financial services group offering insurance, investment, and wealth management products.
  • B. SL4
    SL4 is a branch of Boston’s MBTA Silver Line bus rapid transit service that runs between downtown and the Seaport/South Station area.
  • C. SLD
    SLD was a particle physics experiment at the SLAC Linear Collider that made precision measurements of electroweak interactions, including properties of the Z boson.
  • D. Kismet
    Kismet is a 1955 MGM musical fantasy film directed by Vincente Minnelli, adapted from the Broadway musical set in a stylized, exoticized Baghdad.
  • E. Kismet
    Kismet is an open-source wireless network detector, sniffer, and intrusion detection system widely used for Wi-Fi security auditing and monitoring.
  • 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: SLAM
Triple: [Saint Louis Art Museum, hasNameVariant, SLAM]
Generated description
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SLAM
Target entity description: SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
  • A. SLM
    SLM is the stock ticker symbol for Sanlam, a major South African financial services group offering insurance, investment, and wealth management products.
  • B. SL4
    SL4 is a branch of Boston’s MBTA Silver Line bus rapid transit service that runs between downtown and the Seaport/South Station area.
  • C. SLD
    SLD was a particle physics experiment at the SLAC Linear Collider that made precision measurements of electroweak interactions, including properties of the Z boson.
  • D. Kismet
    Kismet is a 1955 MGM musical fantasy film directed by Vincente Minnelli, adapted from the Broadway musical set in a stylized, exoticized Baghdad.
  • E. Kismet
    Kismet is an open-source wireless network detector, sniffer, and intrusion detection system widely used for Wi-Fi security auditing and monitoring.
  • 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_69a885fdcb9c819081ce6f0b8cd477dd completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9092e46748190b27be7d1aba07dff completed March 5, 2026, 4:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad46a848ec819085c82be8eaea2044 completed March 8, 2026, 9:51 a.m.
NEDg Description generation batch_69ad4841d278819085507528faeaae3e completed March 8, 2026, 9:58 a.m.
NED2 Entity disambiguation (via description) batch_69ad48ff11d881909fd6e9e40d5f1f38 completed March 8, 2026, 10:01 a.m.
Created at: March 4, 2026, 7:27 p.m.