Triple
T4832539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt Mullenweg |
E107976
|
entity |
| Predicate | founded |
P104
|
FINISHED |
| Object | Akismet |
E124265
|
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: Akismet | Statement: [Matt Mullenweg, founded, Akismet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akismet Context triple: [Matt Mullenweg, founded, Akismet]
-
A.
Akismet
chosen
Akismet is a widely used anti-spam service and plugin, particularly for WordPress, that automatically filters spam comments and form submissions on websites.
-
B.
Automattic
Automattic is a web development and publishing company best known for owning and operating WordPress.com and several other major online platforms and tools.
-
C.
Marketo
Marketo is a leading marketing automation software platform that helps businesses manage and optimize digital marketing campaigns and customer engagement.
-
D.
WordPress
WordPress is a widely used open-source content management system that enables users to create, manage, and publish websites and blogs through a user-friendly, web-based interface.
-
E.
Sprinklr
Sprinklr is a customer experience management and social media analytics software company that helps large enterprises manage and optimize interactions across digital channels.
- 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6cc924e08190b03a7541c629aff9 |
completed | March 20, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4dd744688190a420580e3a8332ff |
completed | March 21, 2026, 7:50 a.m. |
Created at: March 20, 2026, 1:24 p.m.