Triple
T30741705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baronet |
E782707
|
entity |
| Predicate | femaleSpouseStyleOfAddress |
P81456
|
FINISHED |
| Object | Lady |
—
|
LITERAL FINISHED |
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 | Statement: [Baronet, femaleSpouseStyleOfAddress, Lady]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleSpouseStyleOfAddress Context triple: [Baronet, femaleSpouseStyleOfAddress, Lady]
-
A.
honorificPrefixOfSpouse
Indicates that a specified honorific prefix (e.g., Mr., Dr., Lady) is used as the formal title for a person’s spouse.
-
B.
hasSpouseUsingFeminineForm
chosen
Indicates that one entity has a spouse, and this spousal relationship is expressed or recorded using a feminine grammatical form.
-
C.
spouseNameWithTitle
Indicates that a person’s spouse is identified by name together with an associated honorific or title.
-
D.
spouseStyle
Indicates a stylistic or fashion-related relationship or influence that exists between spouses.
-
E.
namedForSpouse
Indicates that one entity is named after the spouse of 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_69f224aeb1588190897d395e8ed2acb8 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68f68da34819092a2e476e5f6ee05 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:38 p.m.