Triple
T13504526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Chiklis |
E320978
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Vegas
Vegas is an American television crime drama series set in 1960s Las Vegas, starring Michael Chiklis alongside Dennis Quaid.
|
E1047189
|
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: Vegas | Statement: [Michael Chiklis, notableWork, Vegas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vegas Context triple: [Michael Chiklis, notableWork, 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.
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.
-
C.
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.
-
D.
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.
-
E.
Reno
Reno is a city in northwestern Nevada known for its casinos, tourism, and proximity to outdoor recreation areas in the Sierra Nevada, including Lake Tahoe.
- 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: Vegas Triple: [Michael Chiklis, notableWork, Vegas]
Generated description
Vegas is an American television crime drama series set in 1960s Las Vegas, starring Michael Chiklis alongside Dennis Quaid.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vegas Target entity description: Vegas is an American television crime drama series set in 1960s Las Vegas, starring Michael Chiklis alongside Dennis Quaid.
-
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.
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.
-
C.
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.
-
D.
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.
-
E.
Reno
Reno is a city in northwestern Nevada known for its casinos, tourism, and proximity to outdoor recreation areas in the Sierra Nevada, including Lake Tahoe.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf810e248190a060481004503f96 |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d9009688190b8f18bb3525c6afd |
completed | May 3, 2026, 2:37 p.m. |
| NEDg | Description generation | batch_69f75ec5101081909652b0c0998b36c8 |
completed | May 3, 2026, 2:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f75f4a3b0c81908c0ca0351771953b |
completed | May 3, 2026, 2:44 p.m. |
Created at: April 9, 2026, 9:43 p.m.