Triple

T36851224
Position Surface form Disambiguated ID Type / Status
Subject Ken Williams E910680 entity
Predicate coFounded P104 FINISHED
Object On-Line Systems
On-Line Systems was the original name of the pioneering computer game company later known as Sierra On-Line, co-founded in 1979 and influential in the development of graphic adventure games.
 E2200895 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: On-Line Systems | Statement: [Ken Williams, coFounded, On-Line Systems]
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: On-Line Systems
Triple: [Ken Williams, coFounded, On-Line Systems]
Generated description
On-Line Systems was the original name of the pioneering computer game company later known as Sierra On-Line, co-founded in 1979 and influential in the development of graphic adventure games.

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_69f76e8033d48190a59274f86f13be48 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7cfac54f0819080ff3e3d3b0a442a completed May 3, 2026, 10:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3dde7c1d94819086061240db254d97 completed June 26, 2026, 2:05 a.m.
NEDg Description generation batch_6a3ddfe25b2c81908281c86fd2b2ae87 completed June 26, 2026, 2:11 a.m.
NED2 Entity disambiguation (via description) batch_6a3ded6d88a48190a8fc6188efed7e3f completed June 26, 2026, 3:09 a.m.
Created at: May 3, 2026, 4:13 p.m.