Triple
T297196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Computer Science Department, Carnegie Mellon University |
E6117
|
entity |
| Predicate | hasSubfieldGroup |
P10896
|
FINISHED |
| Object | theory group |
—
|
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: theory group | Statement: [Computer Science Department, Carnegie Mellon University, hasSubfieldGroup, theory group]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubfieldGroup Context triple: [Computer Science Department, Carnegie Mellon University, hasSubfieldGroup, theory group]
-
A.
hasComponentGroup
Indicates that an entity includes or is associated with a specific group of components treated as a single unit.
-
B.
hasTaskGroup
Indicates that an entity is associated with, or belongs to, a specific task group that organizes or groups related tasks.
-
C.
hasUserGroup
Indicates that a user is associated with, belongs to, or is a member of a specific user group.
-
D.
hasLanguageGroup
Indicates that an entity belongs to, is associated with, or is categorized under a particular language group.
-
E.
hasSubstyle
Indicates that one style is a more specific or subordinate variant of another style within a hierarchical style structure.
- F. None of above. chosen
Provenance (4 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea4778cc8190be7b648a82542891 |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e937af888190a0960708f09ae033 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea4545608190898436c72e10f39d |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.