Triple
T11970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Coast of the United States |
E244
|
entity |
| Predicate | hasLandmark |
P105
|
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: [West Coast of the United States, hasLandmark, Hollywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hollywood Context triple: [West Coast of the United States, hasLandmark, 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.
Los Angeles
Los Angeles is a major U.S. metropolis known for its entertainment industry, cultural diversity, and sprawling urban landscape.
-
C.
Chi-Town
Chi-Town is a popular nickname for the city of Chicago, reflecting its identity as a major cultural and economic hub in the United States.
-
D.
New York City
New York City is the largest city in the United States, a global center of finance, culture, media, and technology.
-
E.
Chocolate City
Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a23ff415ec819082ba80ed3859b71e |
completed | Feb. 28, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a24e54be7481909db32fd9b1e90cca |
completed | Feb. 28, 2026, 2:09 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.