Triple
T19889686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs Lyons |
E477993
|
entity |
| Predicate | relationshipToMickey |
P137720
|
FINISHED |
| Object | employer’s son’s blood brother’s twin |
—
|
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: employer’s son’s blood brother’s twin | Statement: [Mrs Lyons, relationshipToMickey, employer’s son’s blood brother’s twin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMickey Context triple: [Mrs Lyons, relationshipToMickey, employer’s son’s blood brother’s twin]
-
A.
relationshipToMichelle
Indicates the specific type of relationship or connection that an entity has to Michelle.
-
B.
relationshipToPrincess
Indicates the specific familial, social, or romantic connection that one entity has to a princess.
-
C.
relationshipToKenny
Indicates the specific familial, social, or interpersonal connection that one entity has to Kenny.
-
D.
relationshipToLloyd
Indicates the specific type of personal or social relationship an entity has with Lloyd.
-
E.
relationshipToBenny
Indicates the specific type of personal or social relationship that an entity has with Benny.
- 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6590ce9f48190a51c0e5ecc828a06 |
completed | April 20, 2026, 4:49 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:52 p.m.