Triple
T35803189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andre Bennett |
E1035032
|
entity |
| Predicate | relationshipStatusWithShirley |
P206054
|
FINISHED |
| Object | married |
—
|
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: married | Statement: [Andre Bennett, relationshipStatusWithShirley, married]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipStatusWithShirley Context triple: [Andre Bennett, relationshipStatusWithShirley, married]
-
A.
relationshipToShireen
Indicates the specific type of personal or social relationship that an entity has with Shireen.
-
B.
relationshipStatusWithMarnie
Indicates the nature or current state of the relationship that an entity has with Marnie.
-
C.
relationshipStatusWithCarrie
Indicates the current nature or state of an entity’s relationship with Carrie.
-
D.
relationshipStatusWithMichael
Indicates the type or state of the relationship that an entity currently has with Michael.
-
E.
relationshipStatusWithAnna
Indicates the type or state of the relationship that an entity currently has with Anna.
- 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_69f76e169bd081909f16cd8c9ee7870c |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037ce53de881908cf14141cf3bc570 |
completed | May 12, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:06 p.m.