Triple

T23079509
Position Surface form Disambiguated ID Type / Status
Subject CSS Hunley E575427 entity
Predicate hasCrewMember P42937 FINISHED
Object George E. Dixon
George E. Dixon was a Confederate army officer best known as the commander of the H. L. Hunley, the first submarine to sink an enemy warship in combat during the American Civil War.
E1620125 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: George E. Dixon | Statement: [CSS Hunley, hasCrewMember, George E. Dixon]
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: George E. Dixon
Triple: [CSS Hunley, hasCrewMember, George E. Dixon]
Generated description
George E. Dixon was a Confederate army officer best known as the commander of the H. L. Hunley, the first submarine to sink an enemy warship in combat 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_69e245be28d48190ad1348d5a73db37d completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18c66a80481909ebc2ba69f1e4bd9 completed April 29, 2026, 4:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0face1d76c8190b7b709de6ea35769 completed May 22, 2026, 1:09 a.m.
NEDg Description generation batch_6a0fadf24a1c8190bf530988ba1b86de completed May 22, 2026, 1:14 a.m.
NED2 Entity disambiguation (via description) batch_6a0faeb55b6c8190944d1bd621b3f819 completed May 22, 2026, 1:17 a.m.
Created at: April 17, 2026, 3:56 p.m.