Triple
T33130965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mtsensk |
E847866
|
entity |
| Predicate | hasNotableWorkSetIn |
P26983
|
FINISHED |
| Object | Lady Macbeth of the Mtsensk District |
—
|
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: Lady Macbeth of the Mtsensk District | Statement: [Mtsensk, hasNotableWorkSetIn, Lady Macbeth of the Mtsensk District]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableWorkSetIn Context triple: [Mtsensk, hasNotableWorkSetIn, Lady Macbeth of the Mtsensk District]
-
A.
hasNotableWorkSetThere
chosen
Indicates that a notable work (such as a book, film, or other creative piece) is set in or takes place within the referenced location.
-
B.
hasNotableWorkCollection
Indicates that an entity is associated with a collection of its notable works or creations.
-
C.
hasNotableWorkRights
Indicates that an entity holds legal or recognized rights associated with a notable work, such as authorship, ownership, or usage rights.
-
D.
hasNotableWorkExample
Indicates that an entity has a specific notable work cited as an example associated with it.
-
E.
hasNotableWorkSection
Indicates that a notable work is associated with a specific section or part of a larger work or document.
- 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_69f349588f088190b7c9588860f72033 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a01839b071481909f9cc79a6a09387d |
completed | May 11, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_6a017edbed688190baa61cbae5110c8e |
completed | May 11, 2026, 7:01 a.m. |
Created at: May 1, 2026, 1:27 a.m.