Triple
T9110869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Fresh Prince of Bel-Air |
E218596
|
entity |
| Predicate | hasSpinOff |
P7226
|
FINISHED |
| Object | Bel-Air |
E779017
|
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: Bel-Air | Statement: [The Fresh Prince of Bel-Air, hasSpinOff, Bel-Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bel-Air Context triple: [The Fresh Prince of Bel-Air, hasSpinOff, Bel-Air]
-
A.
Bel-Air
Bel-Air is a central public transport interchange in Geneva, Switzerland, serving as a key node for tram and bus connections across the city.
-
B.
Bel-Air
Bel-Air is a Paris Métro station located in the 12th arrondissement of Paris, France.
-
C.
Bel-Air
Bel-Air is an upscale residential and commercial barangay in Makati City, Metro Manila, known for its gated villages and proximity to the central business district.
-
D.
Bel-Air
chosen
Bel-Air is a dramatic reimagining of the 1990s sitcom "The Fresh Prince of Bel-Air," presenting the story of Will's move from West Philadelphia to a wealthy Los Angeles neighborhood in a modern, more serious tone.
-
E.
Hotel Bel-Air
Hotel Bel-Air is a historic, ultra-luxury hotel in Los Angeles known for its secluded garden setting, celebrity clientele, and classic Hollywood glamour.
- 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca847102881908f9d86ce9883fb1a |
completed | April 1, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d047a447dc81908f9d1cb457955c7d |
completed | April 3, 2026, 11:05 p.m. |
Created at: March 30, 2026, 7:16 p.m.