Triple

T11049983
Position Surface form Disambiguated ID Type / Status
Subject The Goonies E261219 entity
Predicate mainCharacter P1183 FINISHED
Object Data
Data is a clever, gadget-obsessed member of the kids' adventure group in the 1985 film "The Goonies," known for using his homemade inventions to help his friends.
 E901322 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: Data | Statement: [The Goonies, mainCharacter, Data]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Data
Context triple: [The Goonies, mainCharacter, Data]
  • A. Data
    Data is an android Starfleet officer in Star Trek: The Next Generation, known for his quest to understand humanity and develop emotions.
  • B. Dati
    Dati is a surname most notably associated with Rachida Dati, a prominent French politician and former Minister of Justice.
  • C. Core Data
    Core Data is Apple’s object graph and persistence framework used in macOS and iOS apps to manage and store model layer data.
  • D. Datu
    Datu is a traditional title for a chieftain or local ruler in pre-colonial Philippine societies.
  • E. Data Quality Services
    Data Quality Services is a SQL Server component that provides tools for defining, managing, and improving the quality and consistency of data through knowledge-based cleansing and matching.
  • 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: Data
Triple: [The Goonies, mainCharacter, Data]
Generated description
Data is a clever, gadget-obsessed member of the kids' adventure group in the 1985 film "The Goonies," known for using his homemade inventions to help his friends.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Data
Target entity description: Data is a clever, gadget-obsessed member of the kids' adventure group in the 1985 film "The Goonies," known for using his homemade inventions to help his friends.
  • A. Data
    Data is an android Starfleet officer in Star Trek: The Next Generation, known for his quest to understand humanity and develop emotions.
  • B. Dati
    Dati is a surname most notably associated with Rachida Dati, a prominent French politician and former Minister of Justice.
  • C. Core Data
    Core Data is Apple’s object graph and persistence framework used in macOS and iOS apps to manage and store model layer data.
  • D. Datu
    Datu is a traditional title for a chieftain or local ruler in pre-colonial Philippine societies.
  • E. Data Quality Services
    Data Quality Services is a SQL Server component that provides tools for defining, managing, and improving the quality and consistency of data through knowledge-based cleansing and matching.
  • 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_69d6aa98650481908609c7c56bfa7902 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79868c78881908c8e3672c05ae7ec completed April 9, 2026, 12:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3aa146b148190a87205e542cc718f completed April 18, 2026, 3:58 p.m.
NEDg Description generation batch_69e3ad0379888190b2f56d36d79bf97d completed April 18, 2026, 4:10 p.m.
NED2 Entity disambiguation (via description) batch_69e3b206c7a4819087eb06faa6e1af21 completed April 18, 2026, 4:32 p.m.
Created at: April 8, 2026, 9:26 p.m.