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.