Triple
T5652841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All Around the Town |
E124544
|
entity |
| Predicate | hasSisterRelationship |
P31639
|
FINISHED |
| Object | Laurie Kenyon and Sarah Kenyon |
—
|
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: Laurie Kenyon and Sarah Kenyon | Statement: [All Around the Town, hasSisterRelationship, Laurie Kenyon and Sarah Kenyon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSisterRelationship Context triple: [All Around the Town, hasSisterRelationship, Laurie Kenyon and Sarah Kenyon]
-
A.
hasSister
chosen
Indicates that one entity is the sister of another entity.
-
B.
hasHalfSibling
Indicates that two individuals share exactly one biological parent in common, making them half-siblings.
-
C.
isEldestSisterOf
Indicates that one person is the oldest female sibling in relation to another person.
-
D.
spouseIsHalfSisterOf
Indicates that the person’s spouse is also their half-sister, sharing exactly one biological parent with them.
-
E.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
- 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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022d8a2588190b10de59edbc8841f |
completed | March 22, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69c01b2274b48190b2ef57ed728f785c |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:42 p.m.