Triple
T32102452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abby Phillips |
E819887
|
entity |
| Predicate | relationshipTypeWith Colt Bennett |
P206186
|
FINISHED |
| Object | romantic partner |
—
|
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: romantic partner | Statement: [Abby Phillips, relationshipTypeWith Colt Bennett, romantic partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Colt Bennett Context triple: [Abby Phillips, relationshipTypeWith Colt Bennett, romantic partner]
-
A.
relationshipWithJohnBennett
Indicates that there exists some specified type of relationship or association between an entity and John Bennett.
-
B.
relationshipTypeWith Eugene Gant
Indicates the specific nature or category of relationship that an entity has with Eugene Gant.
-
C.
relationshipTypeWith Bette Porter
Indicates the specific nature or category of relationship that an entity has with Bette Porter.
-
D.
relationshipTypeWith Corie Bratter
Indicates the specific nature or category of relationship that an entity has with Corie Bratter.
-
E.
relationshipTypeWith Brian Flanagan
Indicates the specific nature or category of relationship that an entity has with Brian Flanagan.
- 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_69f34901106881908ea893ad504a08be |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379eaa540819095a1c5d9f3513f9b |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 12:26 a.m.