Triple
T93448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harvard Kennedy School |
E1877
|
entity |
| Predicate | hasNotableAlumniType |
P4387
|
FINISHED |
| Object | heads of state |
—
|
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: heads of state | Statement: [Harvard Kennedy School, hasNotableAlumniType, heads of state]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableAlumniType Context triple: [Harvard Kennedy School, hasNotableAlumniType, heads of state]
-
A.
hasAlumni
Indicates that an institution or organization is associated with individuals who formerly attended or graduated from it.
-
B.
hasNotableMember
Indicates that a group, organization, or collection includes at least one member who is distinguished or noteworthy in some significant way.
-
C.
hasNobelLaureatesAffiliated
Indicates that one entity has Nobel Prize laureates formally associated or connected with it (e.g., as members, staff, or alumni).
-
D.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
E.
hasNotableResident
Indicates that an entity is or has been a well-known or distinguished resident of a particular place or location.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a2512ef600819084d3c627f0d534f4 |
completed | Feb. 28, 2026, 2:21 a.m. |
| PD | Predicate disambiguation | batch_69a24eb9a5ac8190b1d1300e8c4e3606 |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a2512cc3108190aefe5e624312f7d0 |
completed | Feb. 28, 2026, 2:21 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.