Triple
T34527373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aunt Polly Harrington |
E886434
|
entity |
| Predicate | hasRelationshipToPollyanna |
P175131
|
FINISHED |
| Object | legal guardian |
—
|
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: legal guardian | Statement: [Aunt Polly Harrington, hasRelationshipToPollyanna, legal guardian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipToPollyanna Context triple: [Aunt Polly Harrington, hasRelationshipToPollyanna, legal guardian]
-
A.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
-
B.
haveRelationshipWith
chosen
Indicates that one entity is in some form of defined relationship or association with another entity.
-
C.
hasRelationshipTypeWithNastasyaFilippovna
Indicates that an entity has a specific type of relationship with Nastasya Filippovna.
-
D.
relationToPenelope
Indicates a relational connection that one entity has specifically toward Penelope.
-
E.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
- 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_69f349cd7c148190aa99192b126d1527 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 2:02 a.m.