Triple
T35244120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lily Buffay |
E1017610
|
entity |
| Predicate | relationshipToFrankBuffayJr |
P206887
|
FINISHED |
| Object | stepmother |
—
|
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: stepmother | Statement: [Lily Buffay, relationshipToFrankBuffayJr, stepmother]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToFrankBuffayJr Context triple: [Lily Buffay, relationshipToFrankBuffayJr, stepmother]
-
A.
relationshipToShawnSpencer
Indicates the specific type of personal or social relationship an entity has with Shawn Spencer.
-
B.
relationshipToJaneRizzoli
Indicates the specific familial, social, or professional relationship that one entity has to Jane Rizzoli.
-
C.
relationshipToBelcherChildren
Indicates the specific familial or caretaking relationship an entity has to the Belcher children.
-
D.
hasRelationshipTypeWith Fran Fine
Indicates that an entity is connected to Fran Fine by a specific, categorized type of relationship (e.g., familial, professional, romantic, or social).
-
E.
relationshipTypeWithFrasierCrane
Indicates the specific type or nature of relationship an entity has with Frasier Crane.
- 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.