Triple
T13037206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tim Howes |
E326592
|
entity |
| Predicate | coFounded |
P104
|
FINISHED |
| Object | ClearStory Data |
E1018650
|
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: ClearStory Data | Statement: [Tim Howes, coFounded, ClearStory Data]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ClearStory Data Context triple: [Tim Howes, coFounded, ClearStory Data]
-
A.
ClearStory Data
chosen
ClearStory Data is a data analytics and business intelligence company known for its cloud-based platform that blends and visualizes diverse data sources for fast, collaborative insights.
-
B.
DataPilot
DataPilot is LibreOffice Calc’s pivot table tool for interactively summarizing, analyzing, and reorganizing large data sets.
-
C.
Data61
Data61 is an Australian national data science and digital innovation research organization within CSIRO, focused on advanced analytics, cybersecurity, and emerging technologies.
-
D.
Tamr
Tamr is a data mastering and integration company that uses machine learning to unify and clean large, disparate datasets for enterprises.
-
E.
Zoho Analytics
Zoho Analytics is a cloud-based business intelligence and data analytics platform that enables users to visualize data, create reports and dashboards, and derive insights for decision-making.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d5fbaea8819080ca249159d6c125 |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 8:55 p.m.