Triple

T22594009
Position Surface form Disambiguated ID Type / Status
Subject Lucien Laurin E574623 entity
Predicate trained P3665 FINISHED
Object Amberoid
Amberoid was a Thoroughbred racehorse best known as a Belmont Stakes winner during the 1960s.
 E1544782 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: Amberoid | Statement: [Lucien Laurin, trained, Amberoid]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Amberoid
Context triple: [Lucien Laurin, trained, Amberoid]
  • A. Amber
    Amber is a historic town near Jaipur in Rajasthan, India, renowned for its hilltop Amber Fort and rich Rajput architectural heritage.
  • B. Amber
    Amber is a character from the film "Green Room," a tense horror-thriller about a punk band trapped in a remote venue controlled by violent neo-Nazis.
  • C. Amber
    Amber is a feminine given name derived from the English word for the fossilized tree resin, often associated with a warm, golden color.
  • D. Zipolite
    Zipolite is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its clothing-optional beach, bohemian vibe, and strong Pacific surf.
  • E. Amalga
    Amalga is a small rural town in northern Utah, United States, located in Cache County within the Cache Valley region.
  • 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: Amberoid
Triple: [Lucien Laurin, trained, Amberoid]
Generated description
Amberoid was a Thoroughbred racehorse best known as a Belmont Stakes winner during the 1960s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Amberoid
Target entity description: Amberoid was a Thoroughbred racehorse best known as a Belmont Stakes winner during the 1960s.
  • A. Amber
    Amber is a historic town near Jaipur in Rajasthan, India, renowned for its hilltop Amber Fort and rich Rajput architectural heritage.
  • B. Amber
    Amber is a feminine given name derived from the English word for the fossilized tree resin, often associated with a warm, golden color.
  • C. Amber
    Amber is a character from the film "Green Room," a tense horror-thriller about a punk band trapped in a remote venue controlled by violent neo-Nazis.
  • D. Zipolite
    Zipolite is a small, laid-back beach town on Mexico’s Oaxacan coast, known for its clothing-optional beach, bohemian vibe, and strong Pacific surf.
  • E. Amalga
    Amalga is a small rural town in northern Utah, United States, located in Cache County within the Cache Valley region.
  • 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_69e245bc11308190b69d794d5d1e0bb6 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f16163cb248190b377b110d80a6730 completed April 29, 2026, 1:39 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b3d5e31308190a11bff9270795efd completed May 18, 2026, 4:25 p.m.
NEDg Description generation batch_6a0b3e908a308190af37f63904f38915 completed May 18, 2026, 4:30 p.m.
NED2 Entity disambiguation (via description) batch_6a0b3f8d85cc8190812924d466d18735 completed May 18, 2026, 4:34 p.m.
Created at: April 17, 2026, 2:49 p.m.