Triple

T16958055
Position Surface form Disambiguated ID Type / Status
Subject Trade Marks Act (Singapore) E411355 entity
Predicate abbreviation P43 FINISHED
Object TMA
TMA is the commonly used abbreviation for Singapore’s primary legislation governing the registration, protection, and enforcement of trade marks.
 E1242499 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: TMA | Statement: [Trade Marks Act (Singapore), abbreviation, TMA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TMA
Context triple: [Trade Marks Act (Singapore), abbreviation, TMA]
  • A. TAM
    TAM is a prominent annual conference focused on science, skepticism, and critical thinking, originally organized by the James Randi Educational Foundation.
  • B. TAM
    TAM is the standard abbreviation used for the Tampa Tarpons, a Minor League Baseball team based in Tampa, Florida.
  • C. TAM
    TAM is the former brand name and airline code of LATAM Airlines Brasil, one of Brazil’s largest commercial airlines.
  • D. TAM
    TAM is a Georgian aerospace company based in Tbilisi that designs, manufactures, and services aircraft and related aviation components.
  • E. TMTA
    TMTA was the stock ticker symbol for Transmeta Corporation, a now-defunct American semiconductor company known for its low-power x86-compatible microprocessors.
  • 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: TMA
Triple: [Trade Marks Act (Singapore), abbreviation, TMA]
Generated description
TMA is the commonly used abbreviation for Singapore’s primary legislation governing the registration, protection, and enforcement of trade marks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TMA
Target entity description: TMA is the commonly used abbreviation for Singapore’s primary legislation governing the registration, protection, and enforcement of trade marks.
  • A. TAM
    TAM is a prominent annual conference focused on science, skepticism, and critical thinking, originally organized by the James Randi Educational Foundation.
  • B. TAM
    TAM is the standard abbreviation used for the Tampa Tarpons, a Minor League Baseball team based in Tampa, Florida.
  • C. TAM
    TAM is the former brand name and airline code of LATAM Airlines Brasil, one of Brazil’s largest commercial airlines.
  • D. TAM
    TAM is a Georgian aerospace company based in Tbilisi that designs, manufactures, and services aircraft and related aviation components.
  • E. TMTA
    TMTA was the stock ticker symbol for Transmeta Corporation, a now-defunct American semiconductor company known for its low-power x86-compatible microprocessors.
  • 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_69d886c9c9d481909afe222093641cae completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d01d34d08190a73ce48e9988bd97 completed April 18, 2026, 6:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d4666f14819095fc3bcf5e459b61 completed May 10, 2026, 6:54 p.m.
NEDg Description generation batch_6a00d51835c48190b1a37de6ac25ceaa completed May 10, 2026, 6:57 p.m.
NED2 Entity disambiguation (via description) batch_6a00d59b96108190a0e55f01529a0b64 completed May 10, 2026, 6:59 p.m.
Created at: April 10, 2026, 5:31 a.m.