Triple

T28538819
Position Surface form Disambiguated ID Type / Status
Subject Brazilian Special Operations Command E722238 entity
Predicate hasUnit P35 FINISHED
Object 3rd Special Forces Company
The 3rd Special Forces Company is an elite Brazilian Army special operations unit specializing in high-risk, unconventional warfare and counterterrorism missions.
E1826384 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: 3rd Special Forces Company | Statement: [Brazilian Special Operations Command, hasUnit, 3rd Special Forces Company]
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: 3rd Special Forces Company
Triple: [Brazilian Special Operations Command, hasUnit, 3rd Special Forces Company]
Generated description
The 3rd Special Forces Company is an elite Brazilian Army special operations unit specializing in high-risk, unconventional warfare and counterterrorism missions.

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_69f01a5e42348190b1ffbca26e739c84 completed April 28, 2026, 2:24 a.m.
NER Named-entity recognition batch_69f64fdc4e2081909ab249c11234a143 completed May 2, 2026, 7:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cb6e0663c8190bc359c42401dbde4 completed May 31, 2026, 10:32 p.m.
NEDg Description generation batch_6a1cbaaa69348190a4e8de0490e66edf completed May 31, 2026, 10:48 p.m.
NED2 Entity disambiguation (via description) batch_6a1cbb5d90ec819093705eae50314f33 completed May 31, 2026, 10:51 p.m.
Created at: April 28, 2026, 3:34 a.m.