Triple

T11713871
Position Surface form Disambiguated ID Type / Status
Subject 2d Tanks E278441 entity
Predicate nickname P55 FINISHED
Object 2d Tanks E278441 NE FINISHED

How this triple was built (2 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: 2d Tanks | Statement: [2d Tanks, nickname, 2d Tanks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 2d Tanks
Context triple: [2d Tanks, nickname, 2d Tanks]
  • A. 2d Tanks chosen
    2d Tanks is the commonly used nickname for the U.S. Marine Corps’ 2nd Marine Tank Battalion, a historic armored unit known for its combat service in multiple major conflicts.
  • B. Terminal 2D
    Terminal 2D is a passenger terminal at Paris Charles de Gaulle Airport, serving as one of the facilities handling flights and travelers at this major international hub.
  • C. Tanks a Million
    Tanks a Million is a 1941 American military comedy film best known for starring William Tracy as the fast-talking, regulation-obsessed soldier Dorian "Dodo" Doubleday.
  • D. 2d MLG
    2d MLG is a major logistics formation of the United States Marine Corps responsible for providing supply, maintenance, transportation, and support services to Marine forces.
  • E. Tanker War
    The Tanker War was a phase of the Iran–Iraq War during which both sides attacked oil tankers and merchant shipping in the Persian Gulf to disrupt each other’s economic lifelines and pressure international involvement.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4bf54d88190a8e07fbbf8d9e962 completed April 10, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef838562d08190b9a764e88c50d423 completed April 27, 2026, 3:40 p.m.
Created at: April 8, 2026, 9:40 p.m.