Triple
T23442415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teddy |
E565438
|
entity |
| Predicate | relationshipToLindaBelcher |
P152296
|
FINISHED |
| Object | friend |
—
|
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: friend | Statement: [Teddy, relationshipToLindaBelcher, friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLindaBelcher Context triple: [Teddy, relationshipToLindaBelcher, friend]
-
A.
relationshipToEdnaTurnblad
Indicates the specific familial, social, or personal connection that one entity has to Edna Turnblad.
-
B.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
C.
relationshipToHomer
Indicates the specific familial or social relationship that one entity has to Homer.
-
D.
relationshipToMichelle
Indicates the specific type of relationship or connection that an entity has to Michelle.
-
E.
relationshipToElaineBenes
Indicates a person's specific relational connection (such as friend, partner, or family member) to Elaine Benes.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64654e88190b530958b27b32412 |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
| PDg | Predicate description generation | batch_69f07cbbd7488190ab3c8ae7d0fb68bf |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 17, 2026, 5:51 p.m.