Triple
T28949769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sigma Chi |
E730975
|
entity |
| Predicate | hasUndergraduateChaptersIn |
P7252
|
FINISHED |
| Object | United States |
—
|
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: United States | Statement: [Sigma Chi, hasUndergraduateChaptersIn, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUndergraduateChaptersIn Context triple: [Sigma Chi, hasUndergraduateChaptersIn, United States]
-
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.
hasChapterStructure
Indicates that one entity is organized into chapters or contains a defined chapter-based structure in relation to another entity.
-
E.
numberOfChapters
Indicates the total count of chapters associated with a given entity.
- 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_69f043eb9bcc819091ac7b07aecb6475 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f6db1f3ec48190a82e7d893d3c76ba |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
Created at: April 28, 2026, 8:43 a.m.