Triple
T16251802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Simmons |
E394525
|
entity |
| Predicate | relationshipTypeWithLaura |
P10690
|
FINISHED |
| Object | former 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: former romantic partner | Statement: [George Simmons, relationshipTypeWithLaura, former romantic partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithLaura Context triple: [George Simmons, relationshipTypeWithLaura, former romantic partner]
-
A.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
B.
relationshipTypeWithLorraineBroughton
Indicates the specific nature or category of relationship an entity has with Lorraine Broughton.
-
C.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
relationshipToLaureyWilliams
Indicates the nature or type of relational connection an entity has specifically to Laurey Williams.
-
E.
relationshipTypeWith Alicia Johns
Indicates the specific type or nature of the relationship that an entity has with Alicia Johns.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24596e2208190ad9d9abfa6620ca1 |
completed | April 17, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69e219ee6f6481909663b388dc99770a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:04 a.m.