Triple

T3047892
Position Surface form Disambiguated ID Type / Status
Subject Toyota Production System E83492 entity
Predicate usesConcept P531 FINISHED
Object andon
Andon is a visual and auditory alert system used in lean manufacturing to signal production status and highlight problems so they can be addressed immediately.
 E323027 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: andon | Statement: [Toyota Production System, usesConcept, andon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: andon
Context triple: [Toyota Production System, usesConcept, andon]
  • A. Ant
    Ant is a Java-based build automation tool commonly used to compile, package, and deploy Java applications using XML configuration files.
  • B. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • C. AN
    AN is the vehicle registration code used on license plates for the Ansbach district in the Middle Franconia region of Bavaria, Germany.
  • D. ANA
    ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
  • E. ANA
    ANA is the Portuguese company responsible for managing and operating the main airports in Portugal.
  • 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: andon
Triple: [Toyota Production System, usesConcept, andon]
Generated description
Andon is a visual and auditory alert system used in lean manufacturing to signal production status and highlight problems so they can be addressed immediately.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: andon
Target entity description: Andon is a visual and auditory alert system used in lean manufacturing to signal production status and highlight problems so they can be addressed immediately.
  • A. Ant
    Ant is a Java-based build automation tool commonly used to compile, package, and deploy Java applications using XML configuration files.
  • B. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • C. AN
    AN is the vehicle registration code used on license plates for the Ansbach district in the Middle Franconia region of Bavaria, Germany.
  • D. ANA
    ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
  • E. ANA
    ANA is the Portuguese company responsible for managing and operating the main airports in Portugal.
  • 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_69ad8b24924c8190a9bb6f61d519e4ae completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9baed3848190a8351d9c8c4edc79 completed March 8, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1eef7d1e081908535b7d972a147eb completed March 11, 2026, 10:38 p.m.
NEDg Description generation batch_69b1f0ad56dc81909c96018e34345fda completed March 11, 2026, 10:46 p.m.
NED2 Entity disambiguation (via description) batch_69b1f13a34c88190aa829d8d87a5d29b completed March 11, 2026, 10:48 p.m.
Created at: March 8, 2026, 3:01 p.m.