Triple
T12170564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sigma Alpha Mu |
E289949
|
entity |
| Predicate | hasChaptersAt |
P7252
|
FINISHED |
| Object | numerous universities in North America |
—
|
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: numerous universities in North America | Statement: [Sigma Alpha Mu, hasChaptersAt, numerous universities in North America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChaptersAt Context triple: [Sigma Alpha Mu, hasChaptersAt, numerous universities in North America]
-
A.
containsChapter
Indicates that one entity (typically a larger work or document) includes another entity as a chapter within its structure.
-
B.
hasLocalChaptersIn
chosen
Indicates that an organization maintains one or more local chapters or branches within a specified geographic area or location.
-
C.
hasGeneralChapter
Indicates that an entity is associated with, or contains, a general chapter (a broad or overarching section) within a larger structured document or framework.
-
D.
supportsChapters
Indicates that one entity provides assistance, resources, or endorsement to help establish, maintain, or strengthen chapters of another entity.
-
E.
hadChapterOf
Indicates that an entity (such as a book or document) includes or contains a specific chapter as one of its parts.
- 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_69d6ab4d6c00819095a9a7c35de83cfb |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91621ca6c81908365732f361aef13 |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150e85348190b9b47cda4a17dcd0 |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:50 p.m.