Triple
T34031609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gladys Green |
E872667
|
entity |
| Predicate | relationshipToDaniel |
P205286
|
FINISHED |
| Object | grandmother of the narrator |
—
|
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: grandmother of the narrator | Statement: [Gladys Green, relationshipToDaniel, grandmother of the narrator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToDaniel Context triple: [Gladys Green, relationshipToDaniel, grandmother of the narrator]
-
A.
relationshipToDani
Indicates the specific type of relationship or connection that an entity has to Dani.
-
B.
relationshipToDanGoodman
Indicates the specific type of relationship or connection an entity has to Dan Goodman.
-
C.
relationshipToAlice
Indicates the specific type of relationship or connection that an entity has with Alice.
-
D.
relationshipToMike
Indicates the specific type of personal, social, or familial relationship that an entity has with Mike.
-
E.
relationshipToKenny
Indicates the specific familial, social, or interpersonal connection that one entity has to Kenny.
- 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_69f349a2527c81909a7cd4bda94d70ad |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:51 a.m.