Triple
T25251658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denise Fleming |
E632755
|
entity |
| Predicate | relationshipTypeWithPrestonMeyers |
P181020
|
FINISHED |
| Object | childhood 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: childhood friend | Statement: [Denise Fleming, relationshipTypeWithPrestonMeyers, childhood friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithPrestonMeyers Context triple: [Denise Fleming, relationshipTypeWithPrestonMeyers, childhood friend]
-
A.
relationshipTypeWithChrisMyers
Indicates the specific nature or category of relationship that an entity has with Chris Myers.
-
B.
relationshipTypeWithMarkMcPherson
Indicates the specific nature or category of relationship that an entity has with Mark McPherson.
-
C.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
D.
relationshipTypeWith Kate Mercer
Indicates the specific nature or category of the relationship that an entity has with Kate Mercer.
-
E.
relationshipToAmyJuergens
Indicates the specific interpersonal or familial connection that an entity has to Amy Juergens.
- 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_69e75a8fdd3881909ba0b05aa5da92a7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
| PDg | Predicate description generation | batch_69f760a2a90c8190b8fbc55412ab752b |
completed | May 3, 2026, 2:50 p.m. |
Created at: April 21, 2026, 1:11 p.m.