Triple
T23086775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishop of Aberdeen |
E575625
|
entity |
| Predicate | hadTemporalities |
P150875
|
FINISHED |
| Object | lands and revenues in Aberdeenshire |
—
|
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: lands and revenues in Aberdeenshire | Statement: [Bishop of Aberdeen, hadTemporalities, lands and revenues in Aberdeenshire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadTemporalities Context triple: [Bishop of Aberdeen, hadTemporalities, lands and revenues in Aberdeenshire]
-
A.
temporality
Indicates the time-related relationship between events or states, such as their order, duration, or simultaneity.
-
B.
temporal
Indicates a relationship that situates one event, state, or entity in time relative to another (e.g., before, after, or during).
-
C.
temporalAspect
Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
-
D.
hasTemporalUse
Indicates that something is used, applicable, or valid only during a specific time or temporal interval.
-
E.
hasTemporalRole
Indicates that an entity participates in a role or function that is defined, constrained, or characterized by a specific time or temporal context.
- 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_69e245bf3e3c819086d3448720efc01b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18da652ac819085919ade6dd7bce4 |
completed | April 29, 2026, 4:48 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:57 p.m.