Triple
T38140790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Burgess |
E952473
|
entity |
| Predicate | relationshipTypeWithMarionTaylor |
P207614
|
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: [Tom Burgess, relationshipTypeWithMarionTaylor, marriage of convenience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithMarionTaylor Context triple: [Tom Burgess, relationshipTypeWithMarionTaylor, marriage of convenience]
-
A.
relationshipTypeWithMarnie
Indicates the specific nature or category of relationship that an entity has with Marnie.
-
B.
relationshipTypeWithJackieTaylor
Indicates the specific type or nature of the relationship that an entity has with Jackie Taylor.
-
C.
relationshipTypeWithTaylorTravis
Indicates the specific nature or category of the relationship that an entity has with Taylor Travis.
-
D.
relationshipTypeWithMaryTyrone
Indicates the specific nature or category of the relationship that an entity has with Mary Tyrone.
-
E.
relationshipToMariane
Indicates the specific type of relationship or connection that an entity has to Mariane.
- 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_69f76f09a7148190a4b91c0bacdc127a |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037df009f4819082e04683e6e8a106 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:21 p.m.