Triple
T19704354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meg Hourihan |
E473172
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Blogger |
—
|
NE NERFINISHED |
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: Blogger | Statement: [Meg Hourihan, notableWork, Blogger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blogger Context triple: [Meg Hourihan, notableWork, Blogger]
-
A.
Blogger
chosen
Blogger is a popular online platform that allows users to create, publish, and manage personal or professional blogs on the web.
-
B.
Naver Blog
Naver Blog is a popular South Korean blogging platform integrated into the Naver portal, allowing users to create and share personal and professional content online.
-
C.
Tumblr
Tumblr is a microblogging and social networking platform known for its highly customizable blogs, fandom communities, and viral multimedia content.
-
D.
WordPress.com
WordPress.com is a popular hosted blogging and website-building platform that allows users to create and manage websites without needing to run their own server software.
-
E.
Weblogs, Inc.
Weblogs, Inc. was an early 2000s online media company and blog network that helped popularize professional blogging across a range of technology and lifestyle topics.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e516dd048190a0b6c93ea3e71f58 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642b998608190a82f23bbf77f7bd2 |
completed | April 20, 2026, 3:14 p.m. |
Created at: April 10, 2026, 1:46 p.m.