Triple
T1431690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurtwood House |
E30459
|
entity |
| Predicate | hasStudentGender |
P5246
|
FINISHED |
| Object | male |
—
|
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: male | Statement: [Hurtwood House, hasStudentGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStudentGender Context triple: [Hurtwood House, hasStudentGender, male]
-
A.
admissionGender
Indicates the gender-based criteria or classification applied in the context of admission or entry decisions.
-
B.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
C.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
D.
hasCoeducation
Indicates that an educational institution includes both male and female students together in its instructional programs.
-
E.
hasStudentBodyType
chosen
Indicates that an educational institution possesses a student body characterized by a particular type or classification.
- F. None of above.
Provenance (3 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_69a498fc69ec8190b61722bd4b67c4d2 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c500a9888190a16fbb1ec97a79c9 |
completed | March 1, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69a4c4771c9481908ae47c959debbe77 |
completed | March 1, 2026, 10:57 p.m. |
Created at: March 1, 2026, 8 p.m.