Triple
T11928970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cupeño language |
E283859
|
entity |
| Predicate | hasSubjectField |
P87101
|
FINISHED |
| Object | linguistics |
—
|
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: linguistics | Statement: [Cupeño language, hasSubjectField, linguistics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectField Context triple: [Cupeño language, hasSubjectField, linguistics]
-
A.
hasMetaSubject
Indicates that an entity is associated with a subject at a meta-level, such as the topic, theme, or aboutness of the entity rather than its direct content or participants.
-
B.
hasTypicalSubject
Indicates that something is commonly or characteristically used as the subject (agent or topic) of a given relation or action.
-
C.
hasPrimarySubject
Indicates that an entity is the main or principal subject associated with another entity or resource.
-
D.
hasMajorField
chosen
Indicates that an entity (such as a person or student) has a primary area of academic or professional specialization.
-
E.
hasSubjectPosition
Indicates that an entity occupies or is assigned to a particular subject role or position within a structure, context, or organization.
- 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_69d6ab2ce9c48190b5d39511b524f666 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903024afc8190a97aa3263dc7d017 |
completed | April 10, 2026, 2:02 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3af0188190bfb22be5c97b3349 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.