Triple

T21299347

Position	Surface form	Disambiguated ID	Type / Status
Subject	Geoffrey VI of Anjou	`E525013`	entity
Predicate	alsoKnownAs	`P39`	FINISHED
Object	Geoffrey of Anjou Geoffrey of Anjou was a 12th-century French nobleman, Count of Anjou and Maine, and the eldest son of King Henry II of England and Eleanor of Aquitaine.	`E1640771`	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: Geoffrey of Anjou | Statement: [Geoffrey VI of Anjou, alsoKnownAs, Geoffrey of Anjou]

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: Geoffrey of Anjou
Triple: [Geoffrey VI of Anjou, alsoKnownAs, Geoffrey of Anjou]

Generated description

Geoffrey of Anjou was a 12th-century French nobleman, Count of Anjou and Maine, and the eldest son of King Henry II of England and Eleanor of Aquitaine.

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_69e0b517e6748190850d6f6ddf323d69`	completed	April 16, 2026, 10:08 a.m.
NER	Named-entity recognition	`batch_69e7385b1c548190b940ded0163ee3ca`	completed	April 21, 2026, 8:42 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0ff820e0908190a5ced13f1ffb7eda`	completed	May 22, 2026, 6:30 a.m.
NEDg	Description generation	`batch_6a0ff93a0dec81909163580a48548e9a`	completed	May 22, 2026, 6:35 a.m.
NED2	Entity disambiguation (via description)	`batch_6a0ff9d952ec81908a5b2640c263e21d`	completed	May 22, 2026, 6:38 a.m.

Created at: April 16, 2026, 4:05 p.m.