Triple
T17689531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Cerrito Plaza |
E440986
|
entity |
| Predicate | hasTenant |
P3277
|
FINISHED |
| Object | Daiso |
—
|
NE NERFINISHED |
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: Daiso | Statement: [El Cerrito Plaza, hasTenant, Daiso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daiso Context triple: [El Cerrito Plaza, hasTenant, Daiso]
-
A.
Daiso
chosen
Daiso is a popular Japanese discount retail chain known for selling a wide variety of household goods and everyday items, often at a uniform low price.
-
B.
Nissho
Nissho was a prominent disciple of the Japanese Buddhist monk Nichiren who helped propagate and systematize Nichiren Buddhism.
-
C.
Nisshoki
Nisshoki, more commonly known as the Hinomaru, is the national flag of Japan featuring a red sun disc centered on a white field.
-
D.
Nishitetsu
Nishitetsu is a major Japanese private railway and bus company based in Fukuoka, operating extensive public transportation networks across the Kyushu region.
-
E.
Mitsushō
Mitsushō was a former town in Hokkaido, Japan, that later became part of the newly created town of Shinhidaka through a municipal merger.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4704a6bb4819083752285baf3b3ee |
completed | April 19, 2026, 6:03 a.m. |
Created at: April 10, 2026, 10:03 a.m.