Triple
T13175095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Castle |
E313077
|
entity |
| Predicate | relationshipWithKateBeckett |
P108892
|
FINISHED |
| Object | professional 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: professional partner | Statement: [Richard Castle, relationshipWithKateBeckett, professional partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithKateBeckett Context triple: [Richard Castle, relationshipWithKateBeckett, professional partner]
-
A.
relationshipWithKat Barton
Indicates the existence or nature of a relationship that an entity has with Kat Barton.
-
B.
relationshipToKateKeller
Indicates the specific familial, social, or interpersonal connection that one entity has to Kate Keller.
-
C.
relationshipToJoeBuck
Indicates the specific familial, social, or professional relationship that one entity has to the person Joe Buck.
-
D.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
E.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
- 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_69d806ac3ee081909b2fd27d060aa974 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc2c0c88190be357811aa8e828d |
completed | April 10, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69d98ceeb22c8190a6be666031d9e5a4 |
completed | April 10, 2026, 11:51 p.m. |
Created at: April 9, 2026, 9:14 p.m.