Triple
T796504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese Americans |
E17033
|
entity |
| Predicate | haveFaced |
P13650
|
FINISHED |
| Object | anti-Asian discrimination |
—
|
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: anti-Asian discrimination | Statement: [Japanese Americans, haveFaced, anti-Asian discrimination]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveFaced Context triple: [Japanese Americans, haveFaced, anti-Asian discrimination]
-
A.
defacedWith
Indicates that one entity has been damaged, marred, or vandalized using another entity as the means or material of defacement.
-
B.
facedBy
Indicates that one entity is oriented toward and directly opposite another entity, such that it is facing it.
-
C.
have
Indicates that one entity possesses, owns, or contains another entity or attribute.
-
D.
has
Indicates that one entity possesses, owns, contains, or includes another entity as part of its state or composition.
-
E.
facingIssue
chosen
Indicates that an entity is currently experiencing, encountering, or dealing with a problem, difficulty, or obstacle.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7b172e88190a26d31c9075b81fb |
completed | March 1, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69a4a5122a008190b0c621b7bc588d41 |
completed | March 1, 2026, 8:44 p.m. |
Created at: March 1, 2026, 7:38 p.m.