Triple

T27141361

Position	Surface form	Disambiguated ID	Type / Status
Subject	Types of A-bar Dependencies	`E681822`	entity
Predicate	relatedWorkAuthor	`P22411`	FINISHED
Object	Luigi Rizzi Luigi Rizzi is an Italian linguist renowned for his influential work in generative syntax, particularly on the theory of A-bar dependencies and the structure of the left periphery of clauses.	`E2295515`	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: Luigi Rizzi | Statement: [Types of A-bar Dependencies, relatedWorkAuthor, Luigi Rizzi]

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: Luigi Rizzi
Triple: [Types of A-bar Dependencies, relatedWorkAuthor, Luigi Rizzi]

Generated description

Luigi Rizzi is an Italian linguist renowned for his influential work in generative syntax, particularly on the theory of A-bar dependencies and the structure of the left periphery of clauses.

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_69eefacca3888190b67238d380e8f28b`	completed	April 27, 2026, 5:57 a.m.
NER	Named-entity recognition	`batch_69f624c1e6088190803b07b579b80680`	completed	May 2, 2026, 4:22 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a7d6652794c8190a4c83d225f5efdfb`	completed	Aug. 13, 2026, 6:38 a.m.
NEDg	Description generation	`batch_6a7d66daa1a08190a086b008e71ba8ab`	completed	Aug. 13, 2026, 6:40 a.m.
NED2	Entity disambiguation (via description)	`batch_6a7d673251888190bd168e0648065fd2`	completed	Aug. 13, 2026, 6:41 a.m.

Created at: April 27, 2026, 9:09 a.m.