Triple
T13780044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Soup |
E331108
|
entity |
| Predicate | notableSegment |
P3574
|
FINISHED |
| Object |
Chat Stew
Chat Stew is a recurring discussion segment from the podcast "The Soup," featuring informal, often humorous conversations on various topics.
|
E1060863
|
NE FINISHED |
How this triple was built (4 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: Chat Stew | Statement: [The Soup, notableSegment, Chat Stew]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chat Stew Context triple: [The Soup, notableSegment, Chat Stew]
-
A.
Table-Talk
Table-Talk is a celebrated collection of essays by William Hazlitt, noted for its incisive literary criticism, personal reflections, and conversational style.
-
B.
chatr
chatr is a Canadian prepaid wireless service brand offering low-cost mobile plans, primarily operated by Rogers Communications.
-
C.
Cleverbot
Cleverbot is an artificial intelligence chatbot known for its human-like conversational abilities and recognition in Turing test–style competitions.
-
D.
Chatterbug
Chatterbug is an online language-learning platform that offers live tutoring and interactive exercises to help users practice and improve foreign language skills.
-
E.
WeMash
WeMash is a digital media platform and company focused on enabling the licensing, remixing, and monetization of video content.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Chat Stew Triple: [The Soup, notableSegment, Chat Stew]
Generated description
Chat Stew is a recurring discussion segment from the podcast "The Soup," featuring informal, often humorous conversations on various topics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chat Stew Target entity description: Chat Stew is a recurring discussion segment from the podcast "The Soup," featuring informal, often humorous conversations on various topics.
-
A.
Table-Talk
Table-Talk is a celebrated collection of essays by William Hazlitt, noted for its incisive literary criticism, personal reflections, and conversational style.
-
B.
chatr
chatr is a Canadian prepaid wireless service brand offering low-cost mobile plans, primarily operated by Rogers Communications.
-
C.
Cleverbot
Cleverbot is an artificial intelligence chatbot known for its human-like conversational abilities and recognition in Turing test–style competitions.
-
D.
Chatterbug
Chatterbug is an online language-learning platform that offers live tutoring and interactive exercises to help users practice and improve foreign language skills.
-
E.
WeMash
WeMash is a digital media platform and company focused on enabling the licensing, remixing, and monetization of video content.
- F. None of above. chosen
Provenance (5 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02460a688190a27874f8d35819c7 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b07670b08190a205d3c7ccb9dded |
completed | May 3, 2026, 8:30 p.m. |
| NEDg | Description generation | batch_69f7b0f912f081908084042860c922cb |
completed | May 3, 2026, 8:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7b16a43188190968d5cdf32e447ec |
completed | May 3, 2026, 8:34 p.m. |
Created at: April 9, 2026, 10:11 p.m.