Triple
T18643178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | About.com |
E455738
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | Dotdash |
—
|
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: Dotdash | Statement: [About.com, operator, Dotdash]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dotdash Context triple: [About.com, operator, Dotdash]
-
A.
Dotdash Meredith
chosen
Dotdash Meredith is a major American digital and print media company known for operating a wide portfolio of lifestyle, home, finance, and health brands and websites.
-
B.
Dotdot
Dotdot is an application-layer IoT standard that provides a universal, IP-based language for smart devices to communicate and interoperate across different networks and ecosystems.
-
C.
Lifewire
Lifewire is a technology-focused website that provides practical guides, reviews, and how-to articles to help users understand and use consumer tech.
-
D.
Dot
Dot is a character or entity connected to Francis, likely appearing in the same narrative, project, or creative work.
-
E.
Dot
Dot is a short animated film created by Aardman Animations that follows a tiny girl navigating a giant, rapidly unraveling world, notable for its innovative use of stop-motion and mobile phone camera technology.
- 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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5500b6d7c8190b807b772980d913c |
completed | April 19, 2026, 9:58 p.m. |
Created at: April 10, 2026, 11:47 a.m.