Triple
T35951996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The King and Four Queens |
E1039752
|
entity |
| Predicate | hasMotherInLawCharacter |
P199332
|
FINISHED |
| Object | Yes |
—
|
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: Yes | Statement: [The King and Four Queens, hasMotherInLawCharacter, Yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotherInLawCharacter Context triple: [The King and Four Queens, hasMotherInLawCharacter, Yes]
-
A.
motherInLaw
Indicates a relationship where one person is the mother of another person's spouse.
-
B.
futureMotherInLaw
Indicates a relationship where one person is the mother of another person's future spouse, i.e., the mother-in-law in a planned or anticipated marriage.
-
C.
grandmotherInLaw
Indicates a relationship where one person is the grandmother of another person’s spouse.
-
D.
isMaternalFigureOf
Indicates a nurturing, protective, and guiding parental-like relationship that one individual has toward another, typically in a motherly role.
-
E.
hasFictionalMother
Indicates that one entity is the fictional mother of another entity within a narrative or fictional context.
- 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_69f76e25ea488190b7cee970b3e70382 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff2eb19ad88190915fbbe08e8bc84e |
completed | May 9, 2026, 12:55 p.m. |
| PD | Predicate disambiguation | batch_69ff2db5dd608190b7b7ba95f19c276c |
completed | May 9, 2026, 12:51 p.m. |
| PDg | Predicate description generation | batch_69ff2eb0c0888190b0e05a03bf06d388 |
completed | May 9, 2026, 12:55 p.m. |
Created at: May 3, 2026, 4:07 p.m.