Triple
T19164695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cindy Morgan |
E469146
|
entity |
| Predicate | appearedIn |
P795
|
FINISHED |
| Object | 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: Vegas | Statement: [Cindy Morgan, appearedIn, Vegas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vegas Context triple: [Cindy Morgan, appearedIn, Vegas]
-
A.
Vegas
chosen
Vegas is an American television crime drama series set in 1960s Las Vegas, starring Michael Chiklis alongside Dennis Quaid.
-
B.
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.
-
C.
Las Vegas, Nevada
Las Vegas, Nevada is a major resort city in the Mojave Desert known for its vibrant nightlife, casinos, entertainment, and luxury hotels.
-
D.
Santiago de las Vegas
Santiago de las Vegas is a town in the municipality of Boyeros, Havana, Cuba, historically known as a suburban settlement of the capital.
-
E.
Paris Las Vegas
Paris Las Vegas is a French-themed hotel and casino on the Las Vegas Strip, known for its replica Eiffel Tower and Parisian-style architecture.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f15e2720819084b1707497db26a2 |
completed | April 20, 2026, 9:26 a.m. |
Created at: April 10, 2026, 12:06 p.m.