Triple

T24373621

Position	Surface form	Disambiguated ID	Type / Status
Subject	Company E, 51st Infantry (Long Range Patrol)	`E614405`	entity
Predicate	parentUnit	`P11471`	FINISHED
Object	51st Infantry Regiment The 51st Infantry Regiment is a unit of the United States Army with a history that includes specialized elements such as long-range patrol companies.	`E1632165`	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: 51st Infantry Regiment | Statement: [Company E, 51st Infantry (Long Range Patrol), parentUnit, 51st Infantry Regiment]

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: 51st Infantry Regiment
Triple: [Company E, 51st Infantry (Long Range Patrol), parentUnit, 51st Infantry Regiment]

Generated description

The 51st Infantry Regiment is a unit of the United States Army with a history that includes specialized elements such as long-range patrol companies.

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_69e2d7e1e010819098b95eb3f905943d`	completed	April 18, 2026, 1:01 a.m.
NER	Named-entity recognition	`batch_69f293d52f3481908bedc5844b3a8c5e`	completed	April 29, 2026, 11:27 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0fd6771c8c8190a1fdf6c0773bd30c`	completed	May 22, 2026, 4:07 a.m.
NEDg	Description generation	`batch_6a0fd901edf481908f708d76fe382f8f`	completed	May 22, 2026, 4:18 a.m.
NED2	Entity disambiguation (via description)	`batch_6a0fd9daf92481908135f46c41cdbbd7`	completed	May 22, 2026, 4:21 a.m.

Created at: April 18, 2026, 2:02 a.m.