Triple
T19889685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs Lyons |
E477993
|
entity |
| Predicate | relationshipToEdward |
P102727
|
FINISHED |
| Object | adoptive mother |
—
|
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: adoptive mother | Statement: [Mrs Lyons, relationshipToEdward, adoptive mother]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToEdward Context triple: [Mrs Lyons, relationshipToEdward, adoptive mother]
-
A.
relationshipToEdwardBloom
chosen
Indicates the nature or type of connection an entity has to Edward Bloom, such as familial, social, or other relational ties.
-
B.
relationshipToEdd
Indicates the specific type of relationship or connection that an entity has to Edd.
-
C.
relationshipTypeWithEdwardDouglas
Indicates the specific nature or category of relationship that an entity has with Edward Douglas.
-
D.
relationshipToEd
Indicates the specific type of relationship or connection that an entity has to Ed.
-
E.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
- 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_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. |
Created at: April 10, 2026, 1:52 p.m.