Triple
T3250210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tea Cake |
E68158
|
entity |
| Predicate | relationshipTypeWithJanieCrawford |
P46835
|
FINISHED |
| Object | romantic partner |
—
|
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 partner | Statement: [Tea Cake, relationshipTypeWithJanieCrawford, romantic partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithJanieCrawford Context triple: [Tea Cake, relationshipTypeWithJanieCrawford, romantic partner]
-
A.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
B.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
C.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
D.
relationshipToLaureyWilliams
Indicates the nature or type of relational connection an entity has specifically to Laurey Williams.
-
E.
portraysRelationship
Indicates that one entity depicts, represents, or illustrates a relationship between other entities.
- 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_69ad858e4c708190aa31d486cfee8a6a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaf40f7908190a450c3136fccb020 |
completed | March 8, 2026, 5:17 p.m. |
| PD | Predicate disambiguation | batch_69ada41837e48190933572165be0ca38 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada525bb2c8190b773efe6d696b6ab |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:09 p.m.