Triple
T15742119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Ramsey |
E381625
|
entity |
| Predicate | startTime (Bishop of Durham) |
P120445
|
FINISHED |
| Object | 1952 |
—
|
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: 1952 | Statement: [Michael Ramsey, startTime (Bishop of Durham), 1952]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startTime (Bishop of Durham) Context triple: [Michael Ramsey, startTime (Bishop of Durham), 1952]
-
A.
startTime (Bishop of Monmouth)
Indicates the point in time at which someone began serving as Bishop of Monmouth.
-
B.
startTime (Archbishop of Wales)
Indicates the point in time at which someone begins serving as Archbishop of Wales.
-
C.
startTime (Archbishop of North and South America)
Indicates the point in time at which the person became Archbishop of North and South America.
-
D.
startTime (Duke of Saxony)
Indicates the point in time at which the individual became the Duke of Saxony.
-
E.
startTime (Duke of Bavaria)
Indicates the point in time at which the person began holding the title or office of Duke of Bavaria.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0b4d01c9c81909f6b611e8144c838 |
completed | April 16, 2026, 10:07 a.m. |
Created at: April 10, 2026, 4:46 a.m.