Triple
T7166896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwendoline Mary Lacey |
E167091
|
entity |
| Predicate | schoolTypeAttended |
P75243
|
FINISHED |
| Object | girls' boarding school |
—
|
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: girls' boarding school | Statement: [Gwendoline Mary Lacey, schoolTypeAttended, girls' boarding school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schoolTypeAttended Context triple: [Gwendoline Mary Lacey, schoolTypeAttended, girls' boarding school]
-
A.
schoolAttended
Indicates that one entity has attended, or been enrolled as a student at, the school represented by the other entity.
-
B.
alsoAttendedSchool
Indicates that two or more entities attended the same school in addition to any other schools they may have attended.
-
C.
hasSchool
Indicates that an entity possesses, is associated with, or is served by a particular school.
-
D.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
E.
educationFacility
Indicates that one entity functions as an institution or place where the other entity receives or provides education or training.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e85a07388190a07054ef12870fa1 |
completed | March 27, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69c6e1cd5c948190a9113b23f7308c21 |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a213508190a40aca39f9eee7d5 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:48 p.m.