Triple

T8221301
Position Surface form Disambiguated ID Type / Status
Subject Avena fatua E192065 entity
Predicate competesWith P1375 FINISHED
Object Avena sativa
Avena sativa is the cultivated oat species widely grown as a cereal crop for human consumption and livestock feed.
 E720504 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: Avena sativa | Statement: [Avena fatua, competesWith, Avena sativa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Avena sativa
Context triple: [Avena fatua, competesWith, Avena sativa]
  • A. Avena fatua
    Avena fatua, commonly known as wild oat, is a widespread annual grass species often considered a weed in agricultural and disturbed habitats.
  • B. Barley
    Barley is a small rural village in the North Hertfordshire district of England, known for its historic buildings and traditional English countryside setting.
  • C. Buckwheat
    Buckwheat is a beloved child character from the classic "Our Gang" (later known as "The Little Rascals") comedy film series, known for his distinctive appearance and humorous personality.
  • D. Triticum aestivum
    Triticum aestivum is the common bread wheat, a major cereal crop globally cultivated for its grain used in flour and numerous food products.
  • E. Flax
    Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
  • 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: Avena sativa
Triple: [Avena fatua, competesWith, Avena sativa]
Generated description
Avena sativa is the cultivated oat species widely grown as a cereal crop for human consumption and livestock feed.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Avena sativa
Target entity description: Avena sativa is the cultivated oat species widely grown as a cereal crop for human consumption and livestock feed.
  • A. Avena fatua
    Avena fatua, commonly known as wild oat, is a widespread annual grass species often considered a weed in agricultural and disturbed habitats.
  • B. Barley
    Barley is a small rural village in the North Hertfordshire district of England, known for its historic buildings and traditional English countryside setting.
  • C. Buckwheat
    Buckwheat is a beloved child character from the classic "Our Gang" (later known as "The Little Rascals") comedy film series, known for his distinctive appearance and humorous personality.
  • D. Triticum aestivum
    Triticum aestivum is the common bread wheat, a major cereal crop globally cultivated for its grain used in flour and numerous food products.
  • E. Flax
    Flax is a neural network library for JAX that provides a flexible, modular framework for building and training machine learning models in Python.
  • 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_69ca82c9a8ac81908b011c38698456e4 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb77c9680081909000fde3c9ae83ea completed March 31, 2026, 7:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd34ca59ec8190b611681ec9dfd97f completed April 1, 2026, 3:07 p.m.
NEDg Description generation batch_69cd37a290508190b598f96220056041 completed April 1, 2026, 3:20 p.m.
NED2 Entity disambiguation (via description) batch_69cd4eb519608190b5d0f534170214b5 completed April 1, 2026, 4:58 p.m.
Created at: March 30, 2026, 5:45 p.m.