Triple
T26547201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ord. Prof. Dr. |
E671567
|
entity |
| Predicate | denotesTenure |
P161579
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Ord. Prof. Dr., denotesTenure, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: denotesTenure Context triple: [Ord. Prof. Dr., denotesTenure, yes]
-
A.
tenureType
Indicates the type or category of tenure or contractual engagement that characterizes the relationship between the involved entities.
-
B.
initialTenure
Indicates the starting period or first phase of someone’s tenure in a role, position, or organization.
-
C.
lifeTenure
Indicates that an individual holds a position or office for the duration of their lifetime, without a fixed term limit or routine reappointment.
-
D.
tenureCharacteristic
Indicates a relationship where a specific attribute or quality characterizes the duration or conditions of someone’s or something’s tenure.
-
E.
laterTenure
Indicates that one entity’s tenure or term of service occurs after another entity’s tenure or term of service.
- 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_69eeb32163f08190af5f81282738e27a |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f61ad613608190855de13501a86007 |
completed | May 2, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69f611ab768c8190b1849c15a3e59dda |
completed | May 2, 2026, 3 p.m. |
| PDg | Predicate description generation | batch_69f61a16b7848190bf20d2be7e5a16c1 |
completed | May 2, 2026, 3:36 p.m. |
Created at: April 27, 2026, 1:45 a.m.