Triple
T19846914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul |
E476886
|
entity |
| Predicate | derivedFrom |
P909
|
FINISHED |
| Object | Paulus |
—
|
NE NERFINISHED |
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: Paulus | Statement: [Paul, derivedFrom, Paulus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paulus Context triple: [Paul, derivedFrom, Paulus]
-
A.
Paulus
chosen
Paulus was an influential Roman jurist whose legal writings significantly shaped later compilations of Roman law.
-
B.
Apostle Paul
Apostle Paul was an early Christian missionary and theologian whose letters form a significant portion of the New Testament and profoundly shaped Christian doctrine.
-
C.
Paul
Paul is the middle-aged American widower portrayed by Marlon Brando in the controversial 1972 film "Last Tango in Paris."
-
D.
Paul
Paul is a fictional character from Wallace Thurman's Harlem Renaissance novel "Infants of the Spring," which satirically portrays life in a bohemian Black artists' colony.
-
E.
Paul
Paul is a central fictional character in the Australian television drama series "The Newsreader," which explores the lives and challenges of 1980s newsroom staff.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65809da2c8190bb579ef42513b74d |
completed | April 20, 2026, 4:44 p.m. |
Created at: April 10, 2026, 1:51 p.m.