Triple
T21379432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kathleen Glynn |
E527302
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
TV Nation
TV Nation is a satirical television news magazine created by Michael Moore that blends documentary-style reporting with political and social commentary.
|
E1481037
|
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: TV Nation | Statement: [Kathleen Glynn, notableWork, TV Nation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TV Nation Context triple: [Kathleen Glynn, notableWork, TV Nation]
-
A.
TV Guide
TV Guide is a long-running American weekly magazine and digital platform that provides television program listings, entertainment news, and industry features.
-
B.
TV Quick
TV Quick is a British television listings magazine known for covering popular TV shows and presenting the annual TV Quick Awards.
-
C.
.tv
.tv is the country-code top-level domain originally assigned to Tuvalu that has become popular worldwide for websites related to television and video content.
-
D.
TV
TV is Apple’s media playback and streaming application for watching movies, TV shows, and other video content across Apple devices.
-
E.
EPG
EPG is a U.S. Army test and evaluation organization focused on assessing and validating electronic and communications systems for military use.
- 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: TV Nation Triple: [Kathleen Glynn, notableWork, TV Nation]
Generated description
TV Nation is a satirical television news magazine created by Michael Moore that blends documentary-style reporting with political and social commentary.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TV Nation Target entity description: TV Nation is a satirical television news magazine created by Michael Moore that blends documentary-style reporting with political and social commentary.
-
A.
TV Guide
TV Guide is a long-running American weekly magazine and digital platform that provides television program listings, entertainment news, and industry features.
-
B.
TV Quick
TV Quick is a British television listings magazine known for covering popular TV shows and presenting the annual TV Quick Awards.
-
C.
.tv
.tv is the country-code top-level domain originally assigned to Tuvalu that has become popular worldwide for websites related to television and video content.
-
D.
TV
TV is Apple’s media playback and streaming application for watching movies, TV shows, and other video content across Apple devices.
-
E.
EPG
EPG is a U.S. Army test and evaluation organization focused on assessing and validating electronic and communications systems for military use.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0cc2b5c8190aa5f20f920523fe9 |
completed | April 22, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09b42473b08190b0cf48ed71c09de4 |
completed | May 17, 2026, 12:27 p.m. |
| NEDg | Description generation | batch_6a09b494bc348190b72a5e862b74711a |
completed | May 17, 2026, 12:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09b60be86c8190ba4e0890da8dadb1 |
completed | May 17, 2026, 12:35 p.m. |
Created at: April 16, 2026, 5:11 p.m.