Triple

T13446529
Position Surface form Disambiguated ID Type / Status
Subject Law & Order: LA E320498 entity
Predicate alsoKnownAs P39 FINISHED
Object LOLA
LOLA is the commonly used abbreviation for "Law & Order: LA," a short-lived spin-off of the long-running "Law & Order" television franchise set in Los Angeles.
E1041060 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: LOLA | Statement: [Law & Order: LA, alsoKnownAs, LOLA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LOLA
Context triple: [Law & Order: LA, alsoKnownAs, LOLA]
  • A. Lola
    Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
  • B. Lola
    Lola is a 1961 French New Wave film directed by Jacques Demy, featuring Corinne Marchand in the title role as a cabaret singer in the port city of Nantes.
  • C. Lola
    Lola is a lethal, acrobatic henchwoman and primary antagonist in the action film "Transporter 2," known for her distinctive red attire and high-impact fight scenes.
  • D. Lola
    Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • E. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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: LOLA
Triple: [Law & Order: LA, alsoKnownAs, LOLA]
Generated description
LOLA is the commonly used abbreviation for "Law & Order: LA," a short-lived spin-off of the long-running "Law & Order" television franchise set in Los Angeles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: LOLA
Target entity description: LOLA is the commonly used abbreviation for "Law & Order: LA," a short-lived spin-off of the long-running "Law & Order" television franchise set in Los Angeles.
  • A. Lola
    Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
  • B. Lola
    Lola is a 1961 French New Wave film directed by Jacques Demy, featuring Corinne Marchand in the title role as a cabaret singer in the port city of Nantes.
  • C. Lola
    Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • D. Lola
    Lola is a lethal, acrobatic henchwoman and primary antagonist in the action film "Transporter 2," known for her distinctive red attire and high-impact fight scenes.
  • E. Lola
    Lola is a Muppet-style character from the Mexican adaptation of Sesame Street, Plaza Sésamo, known for engaging children through songs and educational segments.
  • 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_69d80761e6cc8190a90c844589998ecc completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaef5f610819092cad33ef72075ff completed April 12, 2026, 2:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69f73998221c8190a2d8982a3da28ec9 completed May 3, 2026, 12:03 p.m.
NEDg Description generation batch_69f73a598c6c81908420b00b665e3b08 completed May 3, 2026, 12:06 p.m.
NED2 Entity disambiguation (via description) batch_69f73e0e598c8190b030a45e658a5055 completed May 3, 2026, 12:22 p.m.
Created at: April 9, 2026, 9:40 p.m.