Triple

T9645130
Position Surface form Disambiguated ID Type / Status
Subject TAM medium tank E233174 entity
Predicate manufacturer P490 FINISHED
Object TAMSE
TAMSE is an Argentine state-owned defense manufacturer best known for producing the TAM family of medium tanks and other armored vehicles.
 E812129 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: TAMSE | Statement: [TAM medium tank, manufacturer, TAMSE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TAMSE
Context triple: [TAM medium tank, manufacturer, TAMSE]
  • 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. TAMUT
    TAMUT is a regional public university in Texarkana, Texas, offering undergraduate and graduate programs as part of the Texas A&M University System.
  • 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: TAMSE
Triple: [TAM medium tank, manufacturer, TAMSE]
Generated description
TAMSE is an Argentine state-owned defense manufacturer best known for producing the TAM family of medium tanks and other armored vehicles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TAMSE
Target entity description: TAMSE is an Argentine state-owned defense manufacturer best known for producing the TAM family of medium tanks and other armored vehicles.
  • 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. TAMUT
    TAMUT is a regional public university in Texarkana, Texas, offering undergraduate and graduate programs as part of the Texas A&M University System.
  • 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_69ca848b31648190b57aa55da20285be completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9b7fd2308190803a196ecdc80d76 completed April 1, 2026, 10:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18258489081909f0b328e223777fc completed April 4, 2026, 9:27 p.m.
NEDg Description generation batch_69d1842a39ac8190a43323c29316df08 completed April 4, 2026, 9:35 p.m.
NED2 Entity disambiguation (via description) batch_69d1849df0a481908777fa263fe8bd10 completed April 4, 2026, 9:37 p.m.
Created at: March 30, 2026, 8:12 p.m.