Triple
T30492945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Adventure of the Devil’s Foot |
E775918
|
entity |
| Predicate | hasClergyCharacter |
P185317
|
FINISHED |
| Object | Dr. Leon Sterndale |
—
|
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: Dr. Leon Sterndale | Statement: [The Adventure of the Devil’s Foot, hasClergyCharacter, Dr. Leon Sterndale]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasClergyCharacter Context triple: [The Adventure of the Devil’s Foot, hasClergyCharacter, Dr. Leon Sterndale]
-
A.
hasClergy
Indicates that an organization or institution possesses or is served by members of the clergy.
-
B.
hasClergyType
Indicates the specific category or role of clergy associated with an entity.
-
C.
hasClericalProtagonist
Indicates that the main character in the work is a member of the clergy or holds a religious office.
-
D.
hasClergyOrder
Indicates that an entity is associated with, or belongs to, a specific religious or clerical order.
-
E.
hasNotableClergyman
Indicates that an entity is associated with a clergyman who is distinguished or notable in some recognized way.
- 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_69f22498c5d481908aaea89e6fab8280 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be520f148190ba200bf3dbf40656 |
completed | May 3, 2026, 9:29 p.m. |
Created at: April 29, 2026, 8:14 p.m.