Triple
T23481401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detective Lieutenant Mike Rogo |
E570413
|
entity |
| Predicate | relationshipTypeWithLindaRogo |
P10690
|
FINISHED |
| Object | protective husband |
—
|
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: protective husband | Statement: [Detective Lieutenant Mike Rogo, relationshipTypeWithLindaRogo, protective husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithLindaRogo Context triple: [Detective Lieutenant Mike Rogo, relationshipTypeWithLindaRogo, protective husband]
-
A.
relationshipTypeWithDeedeeRogers
Indicates the specific nature or category of relationship that an entity has with Deedee Rogers.
-
B.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
C.
relationshipTypeWithRogerDeBris
Indicates the specific nature or category of relationship that an entity has with Roger De Bris.
-
D.
relationshipTypeWithRuth Levinson
Indicates the specific nature or category of relationship that an entity has with Ruth Levinson.
-
E.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
- 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_69e245af8a88819084f2704f6d265a92 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a75002008190b02fbffd94e5e8b1 |
completed | April 29, 2026, 6:38 a.m. |
| PD | Predicate disambiguation | batch_69f0620ac3608190b36916261ea50f54 |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 6:03 p.m.