Triple
T14874290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ion Television |
E349825
|
entity |
| Predicate | hasSisterNetwork |
P6991
|
FINISHED |
| Object | Laff |
E349832
|
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: Laff | Statement: [Ion Television, hasSisterNetwork, Laff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laff Context triple: [Ion Television, hasSisterNetwork, Laff]
-
A.
Laff
chosen
Laff is an American digital multicast television network specializing in comedy programming, including sitcoms and comedic movies.
-
B.
Laff-A-Lympics
Laff-A-Lympics is a late-1970s Hanna-Barbera animated television series that parodies the Olympic Games by pitting teams of classic cartoon characters against each other in comedic competitions.
-
C.
Laff of the Party
Laff of the Party is a classic stand-up comedy album by Redd Foxx showcasing his raw, adult-oriented nightclub routines that helped define his reputation as a groundbreaking comedian.
-
D.
Belaugh
Belaugh is a small, picturesque village in Norfolk, England, known for its riverside setting within the Norfolk Broads.
-
E.
Laffing Sal
Laffing Sal is a historic, animatronic laughing woman figure from early 20th-century amusement parks, now preserved as a popular attraction at San Francisco’s Musée Mécanique.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e3e5d48190a132f2cf012b01e2 |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b52c12481908d0173a2a3ed854b |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:55 a.m.