Triple

T18300488
Position Surface form Disambiguated ID Type / Status
Subject Ray E438345 entity
Predicate hasComponent P35 FINISHED
Object Ray Data
Ray Data is a distributed data processing and loading library in the Ray ecosystem designed to handle large-scale datasets for machine learning and AI workloads.
 E1317474 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: Ray Data | Statement: [Ray, hasComponent, Ray Data]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ray Data
Context triple: [Ray, hasComponent, Ray Data]
  • A. Dati
    Dati is a surname most notably associated with Rachida Dati, a prominent French politician and former Minister of Justice.
  • B. Datu
    Datu is a traditional title for a chieftain or local ruler in pre-colonial Philippine societies.
  • C. Data
    Data is an android Starfleet officer in Star Trek: The Next Generation, known for his quest to understand humanity and develop emotions.
  • D. 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.
  • E. Data Sahib
    Data Sahib is a revered Sufi saint associated with the famous Data Darbar shrine in Lahore, Pakistan.
  • 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: Ray Data
Triple: [Ray, hasComponent, Ray Data]
Generated description
Ray Data is a distributed data processing and loading library in the Ray ecosystem designed to handle large-scale datasets for machine learning and AI workloads.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ray Data
Target entity description: Ray Data is a distributed data processing and loading library in the Ray ecosystem designed to handle large-scale datasets for machine learning and AI workloads.
  • A. Dati
    Dati is a surname most notably associated with Rachida Dati, a prominent French politician and former Minister of Justice.
  • B. Datu
    Datu is a traditional title for a chieftain or local ruler in pre-colonial Philippine societies.
  • C. Data
    Data is an android Starfleet officer in Star Trek: The Next Generation, known for his quest to understand humanity and develop emotions.
  • D. 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.
  • E. Data Sahib
    Data Sahib is a revered Sufi saint associated with the famous Data Darbar shrine in Lahore, Pakistan.
  • 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_69d8b915e3e881909125d760c15d0c29 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e5017e88cc8190a969eb628ca1b496 completed April 19, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03bb5e1fb481908a0b98ea130eda71 completed May 12, 2026, 11:44 p.m.
NEDg Description generation batch_6a03bdb3fb3c819095192ac49e809f55 completed May 12, 2026, 11:54 p.m.
NED2 Entity disambiguation (via description) batch_6a03c193a0a08190b33d80d45f3ed0f0 completed May 13, 2026, 12:10 a.m.
Created at: April 10, 2026, 10:35 a.m.