Triple
T12156721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sands Macao |
E289593
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object |
Sands
Sands is a global casino and resort brand best known for its luxury integrated resorts and gaming properties, particularly in Las Vegas and Macao.
|
E963739
|
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: Sands | Statement: [Sands Macao, brand, Sands]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sands Context triple: [Sands Macao, brand, Sands]
-
A.
Sands
Sands is a ruthless, manipulative CIA agent portrayed by Johnny Depp in the action film "Once Upon a Time in Mexico."
-
B.
Sunny Sands
Sunny Sands is a popular sandy beach in Folkestone, Kent, known for its family-friendly atmosphere and traditional seaside charm.
-
C.
Sea of Sand
The Sea of Sand is a vast, otherworldly volcanic sand plain surrounding Mount Bromo in East Java, Indonesia, renowned for its stark, lunar-like landscape.
-
D.
Golden Sands
Golden Sands is a major Black Sea coastal resort in Bulgaria, renowned for its long sandy beaches, warm climate, and vibrant tourist infrastructure near the city of Varna.
-
E.
Sinking Sands
Sinking Sands is a Ghanaian psychological drama film that explores the destructive impact of domestic violence on a young couple’s marriage.
- 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: Sands Triple: [Sands Macao, brand, Sands]
Generated description
Sands is a global casino and resort brand best known for its luxury integrated resorts and gaming properties, particularly in Las Vegas and Macao.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sands Target entity description: Sands is a global casino and resort brand best known for its luxury integrated resorts and gaming properties, particularly in Las Vegas and Macao.
-
A.
Sands
Sands is a ruthless, manipulative CIA agent portrayed by Johnny Depp in the action film "Once Upon a Time in Mexico."
-
B.
Sunny Sands
Sunny Sands is a popular sandy beach in Folkestone, Kent, known for its family-friendly atmosphere and traditional seaside charm.
-
C.
Sea of Sand
The Sea of Sand is a vast, otherworldly volcanic sand plain surrounding Mount Bromo in East Java, Indonesia, renowned for its stark, lunar-like landscape.
-
D.
Golden Sands
Golden Sands is a major Black Sea coastal resort in Bulgaria, renowned for its long sandy beaches, warm climate, and vibrant tourist infrastructure near the city of Varna.
-
E.
Sinking Sands
Sinking Sands is a Ghanaian psychological drama film that explores the destructive impact of domestic violence on a young couple’s marriage.
- 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_69d6ab4c6710819097a9d228382dde43 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d915c1673c8190830cd15525d16869 |
completed | April 10, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f69c8d408190abbc900deb534045 |
completed | May 2, 2026, 1:05 p.m. |
| NEDg | Description generation | batch_69f5fe53d47c8190896a9abf8cc4bc31 |
completed | May 2, 2026, 1:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f5ffc563a08190b95db768df475a3a |
completed | May 2, 2026, 1:44 p.m. |
Created at: April 8, 2026, 9:50 p.m.