Triple

T38428218
Position Surface form Disambiguated ID Type / Status
Subject Third Colorado Cavalry Regiment E903722 entity
Predicate nickname P55 FINISHED
Object Hundred Dazers
Hundred Dazers was the informal nickname of the Third Colorado Cavalry Regiment, a short-lived Union volunteer unit that served during the American Civil War.
E2269932 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: Hundred Dazers | Statement: [Third Colorado Cavalry Regiment, nickname, Hundred Dazers]
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: Hundred Dazers
Triple: [Third Colorado Cavalry Regiment, nickname, Hundred Dazers]
Generated description
Hundred Dazers was the informal nickname of the Third Colorado Cavalry Regiment, a short-lived Union volunteer unit that served during the American Civil War.

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_69f76e6a2024819081aa04f4932f89d2 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69fccdae823481908f484e97d12b26a4 completed May 7, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41c2909b1c819096f2369ae91335e0 completed June 29, 2026, 12:55 a.m.
NEDg Description generation batch_6a41c3c4ef1c8190a88b7bf1a2b782fa completed June 29, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_6a41c6066520819087dbfcda0751628b completed June 29, 2026, 1:10 a.m.
Created at: May 3, 2026, 4:31 p.m.