Triple
T3147659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E! |
E65801
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
Movietime
Movietime was the original name of the American cable television network now known as E!, which focuses on entertainment news and pop culture programming.
|
E331103
|
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: Movietime | Statement: [E!, formerName, Movietime]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Movietime Context triple: [E!, formerName, Movietime]
-
A.
Cinemastar
Cinemastar is a line of hard disk drives produced by HGST, typically designed for consumer and multimedia applications.
-
B.
Matinee Theatre
Matinee Theatre was a 1950s American live anthology television series that presented a different dramatic play each weekday afternoon.
-
C.
Reel Cinemas
Reel Cinemas is a popular cinema chain in Dubai known for its modern multiplex theaters and premium movie-going experiences.
-
D.
Rave Cinemas
Rave Cinemas is a movie theater chain in the United States that operates multiplex cinemas for mainstream film releases.
-
E.
Reel Mall
Reel Mall is a prominent upscale shopping and lifestyle center located in Shanghai’s central Jing’an District.
- 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: Movietime Triple: [E!, formerName, Movietime]
Generated description
Movietime was the original name of the American cable television network now known as E!, which focuses on entertainment news and pop culture programming.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Movietime Target entity description: Movietime was the original name of the American cable television network now known as E!, which focuses on entertainment news and pop culture programming.
-
A.
Cinemastar
Cinemastar is a line of hard disk drives produced by HGST, typically designed for consumer and multimedia applications.
-
B.
Matinee Theatre
Matinee Theatre was a 1950s American live anthology television series that presented a different dramatic play each weekday afternoon.
-
C.
Reel Cinemas
Reel Cinemas is a popular cinema chain in Dubai known for its modern multiplex theaters and premium movie-going experiences.
-
D.
Rave Cinemas
Rave Cinemas is a movie theater chain in the United States that operates multiplex cinemas for mainstream film releases.
-
E.
Reel Mall
Reel Mall is a prominent upscale shopping and lifestyle center located in Shanghai’s central Jing’an District.
- 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_69ad8584485081909ed529e890cadc4a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada59a54188190a2e020fd4004d734 |
completed | March 8, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b224f53bbc81908416272cd48af69e |
completed | March 12, 2026, 2:29 a.m. |
| NEDg | Description generation | batch_69b225896c4c81909e875a2d357e7bc5 |
completed | March 12, 2026, 2:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b225fef06081908dcd8201def1ad95 |
completed | March 12, 2026, 2:33 a.m. |
Created at: March 8, 2026, 3:05 p.m.