Triple
T169378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karen Armstrong |
E3084
|
entity |
| Predicate | hasOccupationHistory |
P4325
|
FINISHED |
| Object | Roman Catholic nun |
—
|
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: Roman Catholic nun | Statement: [Karen Armstrong, hasOccupationHistory, Roman Catholic nun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationHistory Context triple: [Karen Armstrong, hasOccupationHistory, Roman Catholic nun]
-
A.
hadOccupationStatusUntil
Indicates that an entity held a particular occupational status up to, but not necessarily beyond, a specified point in time.
-
B.
workedAs
chosen
Indicates that an entity held a particular job, role, or position, performing work in that capacity.
-
C.
hasHistoricIndustry
Indicates that an entity has been associated with a notable or historically significant industry or industrial activity in the past.
-
D.
hasHistoricalContext
Indicates that something is related to, influenced by, or best understood in light of specific past events, conditions, or time periods.
-
E.
occupationBegan
Indicates the point in time when an entity started holding a particular occupation or job.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258b6f4f88190b1264bbbeb19a29e |
completed | Feb. 28, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_69a25665f5b8819096ca3e084faf976e |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.