Triple
T4449987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sansei |
E96382
|
entity |
| Predicate | hasCommonExperience |
P40775
|
FINISHED |
| Object | reduced fluency in Japanese compared to Nisei |
—
|
LITERAL 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: reduced fluency in Japanese compared to Nisei | Statement: [Sansei, hasCommonExperience, reduced fluency in Japanese compared to Nisei]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonExperience Context triple: [Sansei, hasCommonExperience, reduced fluency in Japanese compared to Nisei]
-
A.
hasNearbyCommon
Indicates that two entities share at least one common element, feature, or connection that is located within a specified nearby distance or vicinity.
-
B.
commonIn
Indicates that something frequently occurs, appears, or is found within a specified context, group, or environment.
-
C.
coexistedWith
Indicates that two entities existed at the same time and in the same context or environment, without implying any specific type of interaction between them.
-
D.
sharesElementsWith
chosen
Indicates that two entities have one or more elements or components in common.
-
E.
hasCommunityOverlap
Indicates that two entities share a portion of the same community, membership base, or audience.
- F. None of above.
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_69b345415ba481908df738e7174448ba |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355d5975c8190bfe8a2d5d2dbf075 |
completed | March 13, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69b34f649df081909d3cc2f6a1b8f282 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:32 p.m.