Triple

T30396579

Position	Surface form	Disambiguated ID	Type / Status
Subject	Susanna Dickinson	`E773234`	entity
Predicate	birthName	`P65`	FINISHED
Object	Susanna Wilkerson Susanna Wilkerson, better known as Susanna Dickinson, was a survivor of the Battle of the Alamo and an important historical figure in early Texas history.	`E1932993`	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: Susanna Wilkerson | Statement: [Susanna Dickinson, birthName, Susanna Wilkerson]

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: Susanna Wilkerson
Triple: [Susanna Dickinson, birthName, Susanna Wilkerson]

Generated description

Susanna Wilkerson, better known as Susanna Dickinson, was a survivor of the Battle of the Alamo and an important historical figure in early Texas history.

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_69f2248facd48190b183c3f3ca6daef7`	completed	April 29, 2026, 3:32 p.m.
NER	Named-entity recognition	`batch_69f685add1a48190b59d888f861cd410`	completed	May 2, 2026, 11:15 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a28bbbe80a08190baf8058309e76ae8`	completed	June 10, 2026, 1:19 a.m.
NEDg	Description generation	`batch_6a28bc947d108190801b827472733dfa`	completed	June 10, 2026, 1:23 a.m.
NED2	Entity disambiguation (via description)	`batch_6a28bd11752881909989925c16498f98`	completed	June 10, 2026, 1:25 a.m.

Created at: April 29, 2026, 8:02 p.m.