Triple
T32367838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacobson School (historical) in Seesen |
E827042
|
entity |
| Predicate | hasPupilDemographic |
P7875
|
FINISHED |
| Object | Jewish students |
—
|
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: Jewish students | Statement: [Jacobson School (historical) in Seesen, hasPupilDemographic, Jewish students]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPupilDemographic Context triple: [Jacobson School (historical) in Seesen, hasPupilDemographic, Jewish students]
-
A.
hasDemographic
chosen
Indicates that an entity is associated with or characterized by a particular demographic group or attribute.
-
B.
hasPupilsGender
Indicates that an entity has pupils whose gender is specified or characterized in some way.
-
C.
hasDemographicPresenceIn
Indicates that a particular demographic group exists or is represented within a specified geographic area or population context.
-
D.
hasDemographicPattern
Indicates that there is a characteristic distribution or trend of attributes (such as age, gender, income, or ethnicity) within a population or group.
-
E.
hasPupilFutureRole
Indicates that a pupil is expected or designated to hold a specific role or position in the future.
- 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_69f349166d548190887b412fe908e2f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f73ae120bc8190bff94d38d7a7a00d |
completed | May 3, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69f73a38d0848190aa5139144b8561c6 |
completed | May 3, 2026, 12:06 p.m. |
Created at: May 1, 2026, 12:50 a.m.