Triple
T24132066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anika Calhoun |
E597980
|
entity |
| Predicate | relationshipTypeWithLuciousLyon |
P154959
|
FINISHED |
| Object | professional and romantic |
—
|
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 and romantic | Statement: [Anika Calhoun, relationshipTypeWithLuciousLyon, professional and romantic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithLuciousLyon Context triple: [Anika Calhoun, relationshipTypeWithLuciousLyon, professional and romantic]
-
A.
hasRelationshipTypeWithJamalLyon
Indicates that an entity has a specific type of interpersonal relationship with Jamal Lyon.
-
B.
relationshipTypeWithAndreLyon
Indicates the specific nature or category of relationship that an entity has with Andre Lyon.
-
C.
relationshipTypeWithLorraineBroughton
Indicates the specific nature or category of relationship an entity has with Lorraine Broughton.
-
D.
relationshipTypeWithTristan
Indicates the specific nature or category of the relationship that an entity has with Tristan.
-
E.
relationshipTypeWithVincent
Indicates the specific nature or category of relationship that an entity has with Vincent.
- 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_69e288c808b881909fed7d18f04bcbbe |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1df78b6f08190809fc154110fa201 |
completed | April 29, 2026, 10:37 a.m. |
| PD | Predicate disambiguation | batch_69f1765650fc8190a6bc1eb512b240bf |
completed | April 29, 2026, 3:09 a.m. |
| PDg | Predicate description generation | batch_69f17c28b684819084eea522126463f8 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 11:25 p.m.