Triple
T16803817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Downing Professor of the Laws of England |
E408426
|
entity |
| Predicate | areaOfRecruitment |
P124698
|
FINISHED |
| Object | legal historians |
—
|
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: legal historians | Statement: [Downing Professor of the Laws of England, areaOfRecruitment, legal historians]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfRecruitment Context triple: [Downing Professor of the Laws of England, areaOfRecruitment, legal historians]
-
A.
areaOfSupport
Indicates the spatial region or domain within which an entity provides support, assistance, or backing to another.
-
B.
coreAreaOf
Indicates that one entity is the central, primary, or most important area or domain of focus for another entity.
-
C.
area of activity
Indicates the domain, field, or sphere in which an entity is active or carries out its primary functions or operations.
-
D.
reportingArea
Indicates that one entity serves as the geographic or organizational area for which information, data, or events about another entity are reported or aggregated.
-
E.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2ca46f88190b56e81d75012496c |
completed | April 18, 2026, 4:35 p.m. |
| PD | Predicate disambiguation | batch_69e319d0fdb8819088425bd82431640f |
completed | April 18, 2026, 5:42 a.m. |
| PDg | Predicate description generation | batch_69e326bac94481908c082117553320f8 |
completed | April 18, 2026, 6:37 a.m. |
Created at: April 10, 2026, 5:22 a.m.