Triple
T123892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Institute Professor at MIT |
E2504
|
entity |
| Predicate | canHoldAppointmentsInMultipleDepartments |
P4927
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Institute Professor at MIT, canHoldAppointmentsInMultipleDepartments, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canHoldAppointmentsInMultipleDepartments Context triple: [Institute Professor at MIT, canHoldAppointmentsInMultipleDepartments, true]
-
A.
canConfirmAppointments
Indicates that an entity has the ability or permission to confirm scheduled appointments.
-
B.
hasAdministrativeUnit
Indicates that one entity possesses, contains, or is associated with another entity that functions as its administrative subdivision or governing unit.
-
C.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
-
D.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
-
E.
combinedOfficeExistedUntil
Indicates that a merged or joint office or position remained in existence up to a specified end time or date.
- 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_69a251b54ea88190b18281669f59b4c0 |
completed | Feb. 28, 2026, 2:23 a.m. |
| NER | Named-entity recognition | batch_69a2573ce0ac8190b49fb31d3d475bf9 |
completed | Feb. 28, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69a2564a54948190ba30bee858173b27 |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256ea776081908fec36c3fdfb8d84 |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:27 a.m.