Triple
T2778661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regius Professor of Modern History at the University of Cambridge |
E61637
|
entity |
| Predicate | hasTenureType |
P22955
|
FINISHED |
| Object | tenured |
—
|
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: tenured | Statement: [Regius Professor of Modern History at the University of Cambridge, hasTenureType, tenured]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTenureType Context triple: [Regius Professor of Modern History at the University of Cambridge, hasTenureType, tenured]
-
A.
tenureType
chosen
Indicates the type or category of tenure or contractual engagement that characterizes the relationship between the involved entities.
-
B.
lifeTenure
Indicates that an individual holds a position or office for the duration of their lifetime, without a fixed term limit or routine reappointment.
-
C.
teamTenure
Indicates the duration or length of time an entity has been part of a particular team.
-
D.
endOfTenure
Indicates the point or event at which an entity’s period of holding a role, position, or office concludes.
-
E.
hasYearType
Indicates a relationship where an entity is associated with a specific classification or category of year (such as calendar, fiscal, academic, or other year type).
- 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdd00b65c8190a8ea444308c4fa2b |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.