Triple
T20157324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claudine Longet |
E491605
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Las Vegas |
—
|
NE NERFINISHED |
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: Las Vegas | Statement: [Claudine Longet, associatedWith, Las Vegas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Las Vegas Context triple: [Claudine Longet, associatedWith, Las Vegas]
-
A.
Bas Vegas
Bas Vegas is a tongue-in-cheek nickname for the Essex town of Basildon, referencing its lively nightlife and entertainment venues in comparison to Las Vegas.
-
B.
Vegas
Vegas is an American television crime drama series set in 1960s Las Vegas, starring Michael Chiklis alongside Dennis Quaid.
-
C.
Vegas
Vegas is a Swedish video lottery terminal brand operated by the state-owned gambling company Svenska Spel, commonly found in bars, restaurants, and gaming venues across Sweden.
-
D.
Las Vegas, Nevada
chosen
Las Vegas, Nevada is a major resort city in the Mojave Desert known for its vibrant nightlife, casinos, entertainment, and luxury hotels.
-
E.
Reno
Reno is a small city located in Parker County in the U.S. state of Texas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667e18a0c8190a2cc2b305da28047 |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:34 p.m.