Triple
T9473662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diana Goodman |
E228455
|
entity |
| Predicate | relationshipToGabeGoodman |
P88307
|
FINISHED |
| Object | mother |
—
|
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: mother | Statement: [Diana Goodman, relationshipToGabeGoodman, mother]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToGabeGoodman Context triple: [Diana Goodman, relationshipToGabeGoodman, mother]
-
A.
relationshipToJosephCooper
Indicates the specific familial, social, or professional relationship that one entity has to Joseph Cooper.
-
B.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
-
C.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
D.
relationshipToBenjy
Indicates the specific type of relationship or connection an entity has to Benjy.
-
E.
relationshipWithTobyFlenderson
Indicates that one entity has some form of relationship, connection, or association with Toby Flenderson.
- 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_69ca847162c48190b079076c9595513c |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7ff0afd08190871b68a88fdbff2b |
completed | April 1, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69cca55f01b081908dc0f12eaa45f832 |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69cca89d0f0c8190b4528990fe708fca |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:54 p.m.