Triple
T21397566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neal Caffrey |
E527825
|
entity |
| Predicate | relationshipWithPeterBurke |
P138294
|
FINISHED |
| Object | professional partnership |
—
|
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: professional partnership | Statement: [Neal Caffrey, relationshipWithPeterBurke, professional partnership]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithPeterBurke Context triple: [Neal Caffrey, relationshipWithPeterBurke, professional partnership]
-
A.
relationshipToPeter
chosen
Indicates the specific type of relationship or connection that an entity has to Peter.
-
B.
relationshipToPete
Indicates the specific type of relationship or connection that an entity has to Pete.
-
C.
roleOfPeter Seligmann
Indicates that the specified role or position is held by Peter Seligmann.
-
D.
relationshipToBobinot
Indicates the nature or type of relationship that one entity has with Bobinot.
-
E.
relationshipTypeWithRobertCohn
Indicates the specific nature or category of relationship that an entity has with Robert Cohn.
- 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_69e0b520ee3c8190abddbee7e37e834c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b16a9c7c819083bd2d298106fdf1 |
completed | April 22, 2026, 11:30 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:14 p.m.