Triple

T22415442
Position Surface form Disambiguated ID Type / Status
Subject Efteling E554110 entity
Predicate hasAttraction P105 FINISHED
Object Python
Python is a classic steel roller coaster in the Efteling theme park in the Netherlands, known for its multiple inversions and status as one of the park’s most iconic thrill rides.
 E1536217 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: Python | Statement: [Efteling, hasAttraction, Python]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Python
Context triple: [Efteling, hasAttraction, Python]
  • A. Python
    Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
  • B. Python
    Python is a monstrous serpent or dragon from Greek mythology, best known for being slain by the god Apollo at Delphi.
  • C. Pythonidae
    Pythonidae is a family of nonvenomous constrictor snakes that includes pythons found across Africa, Asia, and Australia.
  • D. PyPy
    PyPy is a high-performance alternative Python interpreter featuring a Just-In-Time (JIT) compiler designed to significantly speed up the execution of Python programs.
  • E. Pythion
    Pythion was an ancient city of Perrhaebia in northern Thessaly, Greece, likely known for its regional religious and strategic significance.
  • 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: Python
Triple: [Efteling, hasAttraction, Python]
Generated description
Python is a classic steel roller coaster in the Efteling theme park in the Netherlands, known for its multiple inversions and status as one of the park’s most iconic thrill rides.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Python
Target entity description: Python is a classic steel roller coaster in the Efteling theme park in the Netherlands, known for its multiple inversions and status as one of the park’s most iconic thrill rides.
  • A. Python
    Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
  • B. Python
    Python is a monstrous serpent or dragon from Greek mythology, best known for being slain by the god Apollo at Delphi.
  • C. Pythonidae
    Pythonidae is a family of nonvenomous constrictor snakes that includes pythons found across Africa, Asia, and Australia.
  • D. PyPy
    PyPy is a high-performance alternative Python interpreter featuring a Just-In-Time (JIT) compiler designed to significantly speed up the execution of Python programs.
  • E. Pythion
    Pythion was an ancient city of Perrhaebia in northern Thessaly, Greece, likely known for its regional religious and strategic significance.
  • 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_69e11e4e6ce8819085a1e06d886bf21c completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1594615f881909688b02548ee83eb completed April 29, 2026, 1:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0af0e99ca48190ad93201035c819d6 completed May 18, 2026, 10:58 a.m.
NEDg Description generation batch_6a0af2c6151881908f85a7c3c751cea9 completed May 18, 2026, 11:06 a.m.
NED2 Entity disambiguation (via description) batch_6a0b08e411048190a368214c57c35d5e completed May 18, 2026, 12:41 p.m.
Created at: April 16, 2026, 8:46 p.m.