Triple
T35625311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacob Barber |
E1029433
|
entity |
| Predicate | relationshipToLaurieBarber |
P38683
|
FINISHED |
| Object | son |
—
|
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: son | Statement: [Jacob Barber, relationshipToLaurieBarber, son]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToLaurieBarber Context triple: [Jacob Barber, relationshipToLaurieBarber, son]
-
A.
relationshipToLaurie
chosen
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
B.
relationshipToNicoleBarber
Indicates a specified type of personal or professional relationship that an entity has with Nicole Barber.
-
C.
relationshipToLaureyWilliams
Indicates the nature or type of relational connection an entity has specifically to Laurey Williams.
-
D.
relationshipWithMarieBarone
Indicates that one entity has a specified interpersonal relationship with the person Marie Barone.
-
E.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
- F. None of above.
Provenance (3 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_69f76e07bb0c8190968ea2d836fc42c9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a04d8348190a4819666eab42c9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:05 p.m.