Triple

T8282783
Position Surface form Disambiguated ID Type / Status
Subject Marques Colston E193718 entity
Predicate givenName P17 FINISHED
Object Marques
Marques is a given name notably borne by former NFL wide receiver Marques Colston.
 E723369 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: Marques | Statement: [Marques Colston, givenName, Marques]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marques
Context triple: [Marques Colston, givenName, Marques]
  • A. Santonio
    Santonio is a locality or district within the municipality of Piove di Sacco in the Veneto region of northern Italy.
  • B. Lamon
    Lamon is an archaeological site notable for inscriptions in the ancient Venetic language.
  • C. Paquin
    Paquin is a surname most prominently associated with Academy Award–winning actress Anna Paquin.
  • D. Alonzo
    Alonzo is a given name most notably borne by the American mathematician and logician Alonzo Church, a founder of theoretical computer science and lambda calculus.
  • E. Alonzo
    Alonzo is a minor character in Disney’s live-action film "102 Dalmatians," appearing as one of the dogs involved in the story’s adventure.
  • 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: Marques
Triple: [Marques Colston, givenName, Marques]
Generated description
Marques is a given name notably borne by former NFL wide receiver Marques Colston.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marques
Target entity description: Marques is a given name notably borne by former NFL wide receiver Marques Colston.
  • A. Santonio
    Santonio is a locality or district within the municipality of Piove di Sacco in the Veneto region of northern Italy.
  • B. Lamon
    Lamon is an archaeological site notable for inscriptions in the ancient Venetic language.
  • C. Paquin
    Paquin is a surname most prominently associated with Academy Award–winning actress Anna Paquin.
  • D. Alonzo
    Alonzo is a given name most notably borne by the American mathematician and logician Alonzo Church, a founder of theoretical computer science and lambda calculus.
  • E. Alonzo
    Alonzo is a minor character in Disney’s live-action film "102 Dalmatians," appearing as one of the dogs involved in the story’s adventure.
  • 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_69ca82e217a48190880695635c44b2ed completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7aceec8881909cdfa488dfedc0f5 completed March 31, 2026, 7:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd6877b8e481908ec1e0b91a5276f6 completed April 1, 2026, 6:48 p.m.
NEDg Description generation batch_69cd6d5441248190a9e32281dc8e8d62 completed April 1, 2026, 7:09 p.m.
NED2 Entity disambiguation (via description) batch_69cd7e20f71c8190959319c6683a2810 completed April 1, 2026, 8:20 p.m.
Created at: March 30, 2026, 5:51 p.m.