Triple
T34563678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aunt Alicia |
E887412
|
entity |
| Predicate | relationshipToGigi |
P205470
|
FINISHED |
| Object | primary female role model |
—
|
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: primary female role model | Statement: [Aunt Alicia, relationshipToGigi, primary female role model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToGigi Context triple: [Aunt Alicia, relationshipToGigi, primary female role model]
-
A.
relationshipToGinger
Indicates the type or nature of a relationship that one entity has to the entity referred to as Ginger.
-
B.
relationshipToMary
Indicates that one entity stands in a specified personal or social relationship to Mary.
-
C.
relationToBabo
Indicates a relationship or connection that an entity has to the person or entity referred to as "Babo."
-
D.
relationshipToTina
Indicates the specific type of personal or social relationship that an entity has with Tina.
-
E.
relationshipToMaria
Indicates the specific type of relationship or connection that an entity has to Maria.
- 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_69f349d0c4d881908dd0950f5eb9ec0a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fd7aac8190873077e63873aa72 |
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, 2:02 a.m.