Triple
T11363060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cerritos |
E269132
|
entity |
| Predicate | neighboringCity |
P988
|
FINISHED |
| Object | Lakewood |
E266610
|
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: Lakewood | Statement: [Cerritos, neighboringCity, Lakewood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lakewood Context triple: [Cerritos, neighboringCity, Lakewood]
-
A.
Lakewood
Lakewood is a suburban city in Pierce County, Washington, located south of Tacoma within the greater Seattle metropolitan area.
-
B.
Lakewood
Lakewood is a community name commonly associated with suburban towns in the United States, notably lending its name to Lakewood Township in New Jersey.
-
C.
Lakewood
Lakewood is a major suburban city in Colorado, located just west of Denver and known for its residential communities, parks, and proximity to the Rocky Mountains.
-
D.
Lakewood
Lakewood is a densely populated inner-ring suburb of Cleveland, Ohio, known for its historic housing stock, walkable neighborhoods, and vibrant commercial districts along its main corridors.
-
E.
Lakewood
chosen
Lakewood is a suburban city in Los Angeles County, California, known for its post–World War II planned residential developments and proximity to Long Beach.
- 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_69d6aacbe18081909e5fadb50082dd96 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea442e5c8190babfde25540b27e9 |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5565df5508190aeda7d064bceb157 |
completed | April 19, 2026, 10:25 p.m. |
Created at: April 8, 2026, 9:33 p.m.