Triple

T11523704
Position Surface form Disambiguated ID Type / Status
Subject Norman Lloyd Chaplin E273232 entity
Predicate givenName P17 FINISHED
Object Norman
Norman is a masculine given name of English origin that has been borne by numerous notable figures across various fields.
 E1119 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: Norman | Statement: [Norman Lloyd Chaplin, givenName, Norman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Norman
Context triple: [Norman Lloyd Chaplin, givenName, Norman]
  • A. Harquin
    Harquin is a children's book by British author-illustrator John Burningham, known for its imaginative storytelling and distinctive, expressive artwork.
  • B. Aldred
    Aldred is a masculine given name of Old English origin, historically associated with Anglo-Saxon nobles and clerics.
  • C. Farguson
    Farguson is an alternative spelling of the surname Ferguson, which is of Scottish origin.
  • D. Nonnenwerth
    Nonnenwerth is a small Rhine River island in Germany known for its historic monastery and scenic location near Bad Honnef.
  • E. Orleton
    Orleton is a small rural village in Herefordshire, England, known for its historic church and traditional countryside setting near the English–Welsh border.
  • 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: Norman
Triple: [Norman Lloyd Chaplin, givenName, Norman]
Generated description
Norman is a masculine given name of English origin that has been borne by numerous notable figures across various fields.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Norman
Target entity description: Norman is a masculine given name of English origin that has been borne by numerous notable figures across various fields.
  • A. Norman chosen
    Norman is a masculine given name of English origin that became widely used in the English-speaking world.
  • B. Norman
    Norman is a city in central Oklahoma known for its strong ties to meteorology and atmospheric research, including hosting major national weather institutions.
  • C. Norman
    The Normans were a medieval people of Viking origin who settled in northern France and became influential conquerors and rulers across Europe and the Mediterranean, notably shaping the culture and politics of regions such as England, southern Italy, and Sicily.
  • D. Harquin
    Harquin is a children's book by British author-illustrator John Burningham, known for its imaginative storytelling and distinctive, expressive artwork.
  • E. Aldred
    Aldred is a masculine given name of Old English origin, historically associated with Anglo-Saxon nobles and clerics.
  • F. None of above.

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_69d6aae3fbec8190a14632a5df2538b6 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d87fd26648819083de19bcddf8ad69 completed April 10, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69e62562efb88190bbf3c7bbec8233aa completed April 20, 2026, 1:08 p.m.
NEDg Description generation batch_69e62cf5b9988190bc1935993f0111f7 completed April 20, 2026, 1:41 p.m.
NED2 Entity disambiguation (via description) batch_69e66433ddb48190994bb1160b0ff732 completed April 20, 2026, 5:36 p.m.
Created at: April 8, 2026, 9:37 p.m.