Triple

T32898812
Position Surface form Disambiguated ID Type / Status
Subject van Houten E841549 entity
Predicate hasNotableBearer P458 FINISHED
Object Pieter van Houten
Pieter van Houten is a fictional reclusive Dutch author from John Green’s novel "The Fault in Our Stars," known for writing the book within the book, "An Imperial Affliction."
E2034821 NE FINISHED

How this triple was built (2 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: Pieter van Houten | Statement: [van Houten, hasNotableBearer, Pieter van Houten]
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: Pieter van Houten
Triple: [van Houten, hasNotableBearer, Pieter van Houten]
Generated description
Pieter van Houten is a fictional reclusive Dutch author from John Green’s novel "The Fault in Our Stars," known for writing the book within the book, "An Imperial Affliction."

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_69f34945ae408190b72d8118c83beb77 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d075d5008190af365f582980738a completed May 3, 2026, 4:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34e4f71da88190abee3cd4d6ef3f89 completed June 19, 2026, 6:43 a.m.
NEDg Description generation batch_6a34e848a2708190bb75958e6e3a9d3b completed June 19, 2026, 6:57 a.m.
NED2 Entity disambiguation (via description) batch_6a34e89a45d88190b174c62c8548d179 completed June 19, 2026, 6:58 a.m.
Created at: May 1, 2026, 1:19 a.m.