Triple
T1346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollywood |
E26
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Los Angeles |
E715
|
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: Los Angeles | Statement: [Hollywood, locatedIn, Los Angeles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Los Angeles Context triple: [Hollywood, locatedIn, Los Angeles]
-
A.
Los Angeles
chosen
Los Angeles is a major U.S. metropolis known for its entertainment industry, cultural diversity, and sprawling urban landscape.
-
B.
Long Beach
Long Beach is a coastal city in Southern California known for its busy port, waterfront attractions, and diverse urban community within the Los Angeles metropolitan area.
-
C.
San Francisco
San Francisco is a major coastal city in Northern California known for its hilly landscape, iconic Golden Gate Bridge, and role as a historic center of technology and counterculture.
-
D.
Sacramento
Sacramento is the capital city of the U.S. state of California, known for its role as the state’s political center and its historic roots in the Gold Rush era.
-
E.
Hollywood
Hollywood is a famous Los Angeles neighborhood internationally recognized as the historic center of the American film and entertainment industry.
- 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_69a22a285828819081a58308fb963df1 |
completed | Feb. 27, 2026, 11:35 p.m. |
| NER | Named-entity recognition | batch_69a230c560548190a57df2421e233775 |
completed | Feb. 28, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a25aac4900819093912edb0121ff9d |
completed | Feb. 28, 2026, 3:02 a.m. |
Created at: Feb. 27, 2026, 11:36 p.m.