Triple
T12282299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zapier |
E292742
|
entity |
| Predicate | connectsWith |
P37
|
FINISHED |
| Object | Mailchimp |
E662030
|
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: Mailchimp | Statement: [Zapier, connectsWith, Mailchimp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mailchimp Context triple: [Zapier, connectsWith, Mailchimp]
-
A.
Mailchimp
chosen
Mailchimp is a widely used marketing automation and email marketing platform that helps businesses design, send, and analyze digital campaigns.
-
B.
Marketo
Marketo is a leading marketing automation software platform that helps businesses manage and optimize digital marketing campaigns and customer engagement.
-
C.
Zoho Campaigns
Zoho Campaigns is an email marketing and automation platform that helps businesses design, send, and track targeted email campaigns.
-
D.
Adobe Campaign
Adobe Campaign is Adobe’s cross-channel marketing automation platform used to design, orchestrate, and measure personalized customer campaigns across email, mobile, and other channels.
-
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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cf2b09c81908a11581d33f65be0 |
completed | April 10, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e70dec8819098199fbb54d888c1 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.