Triple
T25486956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harriet Westbrook Shelley |
E638736
|
entity |
| Predicate | marriedToPoet |
P161652
|
FINISHED |
| Object | Percy Bysshe Shelley |
—
|
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: Percy Bysshe Shelley | Statement: [Harriet Westbrook Shelley, marriedToPoet, Percy Bysshe Shelley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToPoet Context triple: [Harriet Westbrook Shelley, marriedToPoet, Percy Bysshe Shelley]
-
A.
marriedToPoetOfWar
Indicates that one entity is married to a person who is recognized as the poet of war.
-
B.
marriedToPainter
Indicates that one entity is married to another entity who is a painter.
-
C.
marriedToBeforeFameOf
Indicates that one person was married to another person before the latter became famous.
-
D.
hasAuthorSpouse
Indicates that the spouse of the subject entity is the author of the related work or entity.
-
E.
marriedToPhotographer
Indicates that one person is legally married to another person whose profession or primary role is that of a photographer.
- 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_69e75dbabeac8190bab30628f8b799d4 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6200ac60481909895c61d050b1338 |
completed | May 2, 2026, 4:02 p.m. |
| PD | Predicate disambiguation | batch_69f61b37a5648190b10d33ae205ccfee |
completed | May 2, 2026, 3:41 p.m. |
| PDg | Predicate description generation | batch_69f61f109ef48190873bfe18638d2046 |
completed | May 2, 2026, 3:58 p.m. |
Created at: April 21, 2026, 2:32 p.m.