Triple
T26114832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Zane |
E658795
|
entity |
| Predicate | relationshipTypeWithMikeRoss |
P152732
|
FINISHED |
| Object | from colleagues to spouses |
—
|
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: from colleagues to spouses | Statement: [Rachel Zane, relationshipTypeWithMikeRoss, from colleagues to spouses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithMikeRoss Context triple: [Rachel Zane, relationshipTypeWithMikeRoss, from colleagues to spouses]
-
A.
relationshipTypeWithRossLockhart
Indicates the specific type or nature of the relationship that an entity has with Ross Lockhart.
-
B.
relationshipStatusWithMichael
Indicates the type or state of the relationship that an entity currently has with Michael.
-
C.
relationshipToJaneRizzoli
Indicates the specific familial, social, or professional relationship that one entity has to Jane Rizzoli.
-
D.
relationshipToShawnSpencer
Indicates the specific type of personal or social relationship an entity has with Shawn Spencer.
-
E.
relationshipToMike
chosen
Indicates the specific type of personal, social, or familial relationship that an entity has with Mike.
- 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_69ee5bc20298819099a42be042eb2349 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69fde9fc184c8190bebef35df0e76076 |
completed | May 8, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69fde6e5beb4819094945a695e961d88 |
completed | May 8, 2026, 1:36 p.m. |
Created at: April 26, 2026, 8:04 p.m.