Triple
T24888623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne Bourchier, 7th Baroness Bourchier |
E622929
|
entity |
| Predicate | hadLover |
P23617
|
FINISHED |
| Object | John Lyngfield |
—
|
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: John Lyngfield | Statement: [Anne Bourchier, 7th Baroness Bourchier, hadLover, John Lyngfield]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadLover Context triple: [Anne Bourchier, 7th Baroness Bourchier, hadLover, John Lyngfield]
-
A.
hadPartner
Indicates that an entity was in a romantic or life-partner relationship with another entity at some point in time.
-
B.
hadPartnerType
Indicates that an entity was associated with another entity in a specific type or category of partnership.
-
C.
hasAffairWith
chosen
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
-
D.
hasBeenDatedBy
Indicates that one entity has previously been in a romantic or dating relationship with another entity.
-
E.
hasSex
Indicates that one entity engages in sexual activity with another entity.
- 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_69e2fac597708190a922bf39a49ec70a |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f43043512481909501a3979cac9947 |
completed | May 1, 2026, 4:46 a.m. |
| PD | Predicate disambiguation | batch_69f420fd375c81908ea4a4e60b76ee8f |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:25 a.m.