Triple

T16853292
Position Surface form Disambiguated ID Type / Status
Subject LG Nexus 4 E409725 entity
Predicate codename P2980 FINISHED
Object Mako
Mako is the internal codename used by Google and LG for the Nexus 4 smartphone model.
 E1236277 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: Mako | Statement: [LG Nexus 4, codename, Mako]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mako
Context triple: [LG Nexus 4, codename, Mako]
  • A. Mako
    Mako was a Japanese-American actor and voice actor known for his distinctive voice and roles in films like "Conan the Barbarian" and as the voice of Iroh in "Avatar: The Last Airbender."
  • B. Mako
    Mako is a Japanese imperial family member best known as Princess Mako of Akishino, the former princess who left royal status upon her marriage to a commoner.
  • C. Mako
    Mako is the nickname of Benjamin Mako Hill, a prominent free software activist, scholar, and developer involved with projects like Debian and Wikimedia.
  • D. Mako
    Mako is a central firebending protagonist in *The Legend of Korra*, known for his serious demeanor, leadership in Team Avatar, and complex romantic relationships.
  • E. Mako
    Mako is a high-speed steel roller coaster at SeaWorld Orlando themed around the ocean’s fastest shark.
  • 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: Mako
Triple: [LG Nexus 4, codename, Mako]
Generated description
Mako is the internal codename used by Google and LG for the Nexus 4 smartphone model.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mako
Target entity description: Mako is the internal codename used by Google and LG for the Nexus 4 smartphone model.
  • A. Mako
    Mako was a Japanese-American actor and voice actor known for his distinctive voice and roles in films like "Conan the Barbarian" and as the voice of Iroh in "Avatar: The Last Airbender."
  • B. Mako
    Mako is a Japanese imperial family member best known as Princess Mako of Akishino, the former princess who left royal status upon her marriage to a commoner.
  • C. Mako
    Mako is a high-speed steel roller coaster at SeaWorld Orlando themed around the ocean’s fastest shark.
  • D. Mako
    Mako is the nickname of Benjamin Mako Hill, a prominent free software activist, scholar, and developer involved with projects like Debian and Wikimedia.
  • E. Mako
    Mako is a central firebending protagonist in *The Legend of Korra*, known for his serious demeanor, leadership in Team Avatar, and complex romantic relationships.
  • 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_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b37bbb80819086d844a313625cad completed April 18, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb216fac81909d401c6b9911d1e0 completed May 10, 2026, 5:06 p.m.
NEDg Description generation batch_6a00bb9b2b1881908f9f5c3dd1a2d500 completed May 10, 2026, 5:08 p.m.
NED2 Entity disambiguation (via description) batch_6a00bc3a4b888190bd190b9330e2777d completed May 10, 2026, 5:11 p.m.
Created at: April 10, 2026, 5:24 a.m.