Triple
T433906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonardo DiCaprio |
E9770
|
entity |
| Predicate | activeIn |
P1560
|
FINISHED |
| Object | Hollywood |
E247
|
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: Hollywood | Statement: [Leonardo DiCaprio, activeIn, Hollywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hollywood Context triple: [Leonardo DiCaprio, activeIn, Hollywood]
-
A.
Hollywood
chosen
Hollywood is a famous Los Angeles neighborhood internationally recognized as the historic center of the American film and entertainment industry.
-
B.
West Hollywood
West Hollywood is an independent city in Los Angeles County known for its vibrant nightlife, LGBTQ+ community, and iconic Sunset Strip.
-
C.
East Hollywood
East Hollywood is a diverse, densely populated neighborhood in central Los Angeles known for its mix of residential areas, ethnic enclaves, and proximity to major Hollywood landmarks.
-
D.
Hollywood North
Hollywood North is a popular nickname for Vancouver, Canada, reflecting its status as a major center for film and television production.
-
E.
Burbank, California
Burbank, California is a city in Los Angeles County known as a major center of the entertainment industry, home to numerous film and television studios and media companies.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ef0a008c8190ae0aa25e4df9c35f |
completed | Feb. 28, 2026, 1:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a48a7526fc8190b2aa91606319909b |
completed | March 1, 2026, 6:50 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.