Triple
T9008248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aviation/Century station |
E215398
|
entity |
| Predicate | hasCity |
P316
|
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: [Aviation/Century station, hasCity, Los Angeles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Los Angeles Context triple: [Aviation/Century station, hasCity, 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.
Los Ángeles
Los Ángeles is a mid-sized Chilean city known as an important commercial and agricultural center in the south-central part of the country.
-
C.
San Angeles
San Angeles is a fictional futuristic megacity formed from the merger of Los Angeles and San Diego in the science fiction film "Demolition Man."
-
D.
San Fransokyo
San Fransokyo is a fictional futuristic hybrid city combining elements of San Francisco and Tokyo, serving as the primary setting of Disney's animated film "Big Hero 6."
-
E.
Santa Monica
Santa Monica is a coastal city in western Los Angeles County, California, known for its iconic pier, beaches, and vibrant tourism and entertainment scene.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69bed8588190afc9cbca12b75a3b |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfd095a07c81908e164cfa68fb693e |
completed | April 3, 2026, 2:37 p.m. |
Created at: March 30, 2026, 7:06 p.m.