Triple
T35244134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duncan Sullivan |
E1017611
|
entity |
| Predicate | relationshipTypeWithPhoebeBuffay |
P206888
|
FINISHED |
| Object | marriage of convenience |
—
|
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: marriage of convenience | Statement: [Duncan Sullivan, relationshipTypeWithPhoebeBuffay, marriage of convenience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithPhoebeBuffay Context triple: [Duncan Sullivan, relationshipTypeWithPhoebeBuffay, marriage of convenience]
-
A.
relationshipTypeWithLorelai
Indicates the specific nature or category of relationship that an entity has with Lorelai.
-
B.
relationshipToSheldonCooper
Indicates the specific interpersonal or familial connection that an entity has to Sheldon Cooper.
-
C.
relationshipTypeWithFrasierCrane
Indicates the specific type or nature of relationship an entity has with Frasier Crane.
-
D.
relationshipWithTobyFlenderson
Indicates that one entity has some form of relationship, connection, or association with Toby Flenderson.
-
E.
relationshipToRebecca
Indicates the specific type of relationship or connection an entity has to Rebecca.
- 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_69f76de235048190b990070c23c51b6b |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:02 p.m.