Triple
T33185034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Cugat |
E849443
|
entity |
| Predicate | hasRelationshipTypeWithLizCugat |
P204236
|
FINISHED |
| Object | comic marital misunderstandings |
—
|
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: comic marital misunderstandings | Statement: [George Cugat, hasRelationshipTypeWithLizCugat, comic marital misunderstandings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithLizCugat Context triple: [George Cugat, hasRelationshipTypeWithLizCugat, comic marital misunderstandings]
-
A.
hasRelationshipTypeWithLukas
Indicates that an entity has a specific type of relationship or connection with Lukas.
-
B.
hasRelationshipTypeWithAglayaIvanovna
Indicates that an entity has a specific type of relationship or connection with Aglaya Ivanovna.
-
C.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
D.
hasRelationshipTypeWithAngélique
Indicates that one entity has a specific type of relationship or relational status with Angélique.
-
E.
hasRelationshipTypeWithNastasyaFilippovna
Indicates that an entity has a specific type of relationship with Nastasya Filippovna.
- 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_69f3495e0f108190a6a7006f79f9c2c3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a0353887e848190ad98ecbbe7bbc061 |
completed | May 12, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_6a0352dfb5648190b9f8c9b7c388d2a1 |
completed | May 12, 2026, 4:18 p.m. |
| PDg | Predicate description generation | batch_6a0353877d4c8190a461118bc66d6b09 |
completed | May 12, 2026, 4:21 p.m. |
Created at: May 1, 2026, 1:29 a.m.