Triple

T24086137

Position	Surface form	Disambiguated ID	Type / Status
Subject	Lew Cirne	`E596648`	entity
Predicate	previouslyFounded	`P96309`	FINISHED
Object	Wily Technology Wily Technology was a software company best known for its application performance management tools that helped monitor and optimize the performance of enterprise Java applications.	`E1616624`	NE FINISHED

How this triple was built (3 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: Wily Technology | Statement: [Lew Cirne, previouslyFounded, Wily Technology]

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: Wily Technology
Triple: [Lew Cirne, previouslyFounded, Wily Technology]

Generated description

Wily Technology was a software company best known for its application performance management tools that helped monitor and optimize the performance of enterprise Java applications.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: previouslyFounded
Context triple: [Lew Cirne, previouslyFounded, Wily Technology]

A. foundedByFormer chosen
Indicates that an entity was founded by someone who previously held a specified role or affiliation (e.g., a former employee, member, or official) of another entity.
B. foundedBefore
Indicates that one entity was established or created at an earlier time than the other entity.
C. possiblyFoundedBy
Indicates that an entity may have been founded or established by another entity, but this founding relationship is uncertain or not definitively confirmed.
D. previouslyEstablished
Indicates that the relationship or condition it connects has already been formed or confirmed at an earlier time before the current context.
E. foundedFor
Indicates that an entity was established or created specifically to serve, support, or benefit another entity or purpose.
F. None of above.

Provenance (6 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_69e288c4638c81909bacc28a1e3d436b`	completed	April 17, 2026, 7:23 p.m.
NER	Named-entity recognition	`batch_69f1dc2a76c881908ee6e599dc6d155e`	completed	April 29, 2026, 10:23 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0f9674441c819084d8ba6128cecc5f`	completed	May 21, 2026, 11:34 p.m.
NEDg	Description generation	`batch_6a0f971aed7c8190a38b36f96f284bb3`	completed	May 21, 2026, 11:36 p.m.
NED2	Entity disambiguation (via description)	`batch_6a0f98a259a481908e48de61aab3fc72`	completed	May 21, 2026, 11:43 p.m.
PD	Predicate disambiguation	`batch_69f17651458c8190bbfd301883e46085`	completed	April 29, 2026, 3:09 a.m.

Created at: April 17, 2026, 10:44 p.m.