Triple

T269567
Position Surface form Disambiguated ID Type / Status
Subject Universal Camouflage Pattern E5600 entity
Predicate alsoKnownAs P39 FINISHED
Object UCP
UCP (Universal Camouflage Pattern) is a digital camouflage design formerly used by the U.S. Army, intended to provide concealment across multiple environments with a single uniform pattern.
 E34911 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: UCP | Statement: [Universal Camouflage Pattern, alsoKnownAs, UCP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UCP
Context triple: [Universal Camouflage Pattern, alsoKnownAs, UCP]
  • A. USCP
    USCP is the federal law enforcement agency responsible for protecting the U.S. Capitol complex, its members, staff, and visitors.
  • B. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • C. UNP
    UNP is the stock ticker symbol for Union Pacific Corporation, one of the largest freight railroad companies in the United States.
  • D. NCP
    NCP (Network Control Protocol) was an early host-to-host communication protocol suite that enabled data exchange between computers on the ARPANET before the adoption of TCP/IP.
  • E. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • 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: UCP
Triple: [Universal Camouflage Pattern, alsoKnownAs, UCP]
Generated description
UCP (Universal Camouflage Pattern) is a digital camouflage design formerly used by the U.S. Army, intended to provide concealment across multiple environments with a single uniform pattern.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UCP
Target entity description: UCP (Universal Camouflage Pattern) is a digital camouflage design formerly used by the U.S. Army, intended to provide concealment across multiple environments with a single uniform pattern.
  • A. USCP
    USCP is the federal law enforcement agency responsible for protecting the U.S. Capitol complex, its members, staff, and visitors.
  • B. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • C. UNP
    UNP is the stock ticker symbol for Union Pacific Corporation, one of the largest freight railroad companies in the United States.
  • D. NCP
    NCP (Network Control Protocol) was an early host-to-host communication protocol suite that enabled data exchange between computers on the ARPANET before the adoption of TCP/IP.
  • E. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • 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_69a25853594c8190b05ec3a586ec88bf completed Feb. 28, 2026, 2:52 a.m.
NER Named-entity recognition batch_69a25db00ed48190be6f598807265356 completed Feb. 28, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69a38b9220f08190a94ffd00bb7425fd completed March 1, 2026, 12:42 a.m.
NEDg Description generation batch_69a38cb1d5808190b548efd55de09523 completed March 1, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_69a38d10782c8190beead28963b003ab completed March 1, 2026, 12:49 a.m.
Created at: Feb. 28, 2026, 2:57 a.m.