Triple
T28799278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stacey Colbert |
E727183
|
entity |
| Predicate | relationshipTypeWithNedDorsey |
P202415
|
FINISHED |
| Object | roommates |
—
|
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: roommates | Statement: [Stacey Colbert, relationshipTypeWithNedDorsey, roommates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithNedDorsey Context triple: [Stacey Colbert, relationshipTypeWithNedDorsey, roommates]
-
A.
hasRelationshipTypeWith Frank Drebin
Indicates that there exists a specific type of relationship between an entity and Frank Drebin.
-
B.
relationshipToDeloris
Indicates the specific type of personal, familial, or social relationship that one entity has with the entity named Deloris.
-
C.
relationshipTypeWithJodieDallas
Indicates the specific nature or category of relationship that an entity has with Jodie Dallas.
-
D.
relationshipTypeWithEdwardDouglas
Indicates the specific nature or category of relationship that an entity has with Edward Douglas.
-
E.
relationshipToNedSchneebly
Indicates the specific type of personal, social, or professional relationship an entity has with Ned Schneebly.
- 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_69f0319b7c44819085736bcc256185e6 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_6a007dd341108190a1d03eab46041694 |
completed | May 10, 2026, 12:45 p.m. |
| PD | Predicate disambiguation | batch_6a007b1fe2a881909ec50a1e65e4651b |
completed | May 10, 2026, 12:33 p.m. |
| PDg | Predicate description generation | batch_6a007dd263b48190bd5de5386d45858b |
completed | May 10, 2026, 12:45 p.m. |
Created at: April 28, 2026, 6:26 a.m.