Triple

T3081816
Position Surface form Disambiguated ID Type / Status
Subject RFC 3168 E64274 entity
Predicate relatedConcept P37 FINISHED
Object RED
RED (Random Early Detection) is an active queue management algorithm used in networking to preemptively drop packets and control congestion before router buffers overflow.
 E325873 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: RED | Statement: [RFC 3168, relatedConcept, RED]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RED
Context triple: [RFC 3168, relatedConcept, RED]
  • A. Red
    Red is the famous nickname of Arnold "Red" Auerbach, the legendary Boston Celtics coach and executive known for his pivotal role in building an NBA dynasty.
  • B. Red
    Red is the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
  • C. Red
    Red is the nickname of Red Rolfe, an American Major League Baseball third baseman best known for his years with the New York Yankees in the 1930s and 1940s.
  • D. Red
    Red is Taylor Swift’s critically acclaimed 2012 studio album that marked her transition from country to mainstream pop with emotionally charged, genre-blending songs.
  • E. Red
    Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
  • 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: RED
Triple: [RFC 3168, relatedConcept, RED]
Generated description
RED (Random Early Detection) is an active queue management algorithm used in networking to preemptively drop packets and control congestion before router buffers overflow.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RED
Target entity description: RED (Random Early Detection) is an active queue management algorithm used in networking to preemptively drop packets and control congestion before router buffers overflow.
  • A. Red
    Red is the famous nickname of Arnold "Red" Auerbach, the legendary Boston Celtics coach and executive known for his pivotal role in building an NBA dynasty.
  • B. Red
    Red is the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
  • C. Red
    Red is the nickname of Red Rolfe, an American Major League Baseball third baseman best known for his years with the New York Yankees in the 1930s and 1940s.
  • D. Red
    Red is Taylor Swift’s critically acclaimed 2012 studio album that marked her transition from country to mainstream pop with emotionally charged, genre-blending songs.
  • E. Red
    Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
  • 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_69ad857bb4c88190a4cf27893fcabed8 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada1e70b9081908c801d084a6ae992 completed March 8, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f89847e48190b82849701e119758 completed March 11, 2026, 11:19 p.m.
NEDg Description generation batch_69b1f9608e88819098f4044e54e0d908 completed March 11, 2026, 11:23 p.m.
NED2 Entity disambiguation (via description) batch_69b1fe3c8f408190988e7c7e3a51057e completed March 11, 2026, 11:43 p.m.
Created at: March 8, 2026, 3:03 p.m.