Triple
T38173702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Douai School |
E1000146
|
entity |
| Predicate | hasLayStaff |
P201664
|
FINISHED |
| Object | lay teachers |
—
|
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: lay teachers | Statement: [Douai School, hasLayStaff, lay teachers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLayStaff Context triple: [Douai School, hasLayStaff, lay teachers]
-
A.
hasLayOffice
Indicates that an individual holds or has held a non-ordained (lay) official position or role within an organization or institution.
-
B.
hasLayMembersCount
Indicates the number of lay (non-clergy or non-professional) members associated with an entity.
-
C.
hasLocalStaffFrom
Indicates that an organization or entity employs or is staffed by people originating from a specified local area or region.
-
D.
hasLayCollaborators
Indicates that an entity is supported or assisted by non-professional or non-specialist collaborators in carrying out its activities or functions.
-
E.
hasStaffingStatus
Indicates the current staffing condition or level associated with an entity, such as whether it is adequately, under-, or over-staffed.
- 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_69f76daaace48190a38cee37f8ce343f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a0010e46d948190a51111b5270fade7 |
completed | May 10, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_6a001061d34c8190bfe73f3d7c061eb7 |
completed | May 10, 2026, 4:58 a.m. |
| PDg | Predicate description generation | batch_6a0010e304a08190a4d0a4fa11a9a3b3 |
completed | May 10, 2026, 5 a.m. |
Created at: May 3, 2026, 4:29 p.m.