Triple
T22751596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiana Brown |
E562714
|
entity |
| Predicate | relationshipTypeWithAndreLyon |
P149602
|
FINISHED |
| Object | romantic relationship |
—
|
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: romantic relationship | Statement: [Tiana Brown, relationshipTypeWithAndreLyon, romantic relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithAndreLyon Context triple: [Tiana Brown, relationshipTypeWithAndreLyon, romantic relationship]
-
A.
hasRelationshipTypeWithJamalLyon
Indicates that an entity has a specific type of interpersonal relationship with Jamal Lyon.
-
B.
relationshipToLouisHinds
Indicates the specific familial, social, or professional relationship that an entity has to Louis Hinds.
-
C.
relationshipTypeWithAnneLouvet
Indicates the specific type or nature of the relationship that an entity has with Anne Louvet.
-
D.
relationshipTypeWithVincent
Indicates the specific nature or category of relationship that an entity has with Vincent.
-
E.
relationshipTypeWith Alonzo Harris
Indicates the specific nature or category of the relationship that an entity has with Alonzo Harris.
- 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_69e24551ec7881909a9c924dbea155f6 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f179b9ac348190bff4dc470931f7e3 |
completed | April 29, 2026, 3:23 a.m. |
| PD | Predicate disambiguation | batch_69eed2b88d88819096015deb6a648801 |
completed | April 27, 2026, 3:06 a.m. |
| PDg | Predicate description generation | batch_69eeeb5681f88190821129ced752f190 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:24 p.m.