Triple
T25178170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Changez |
E630507
|
entity |
| Predicate | relationshipTypeWithErica |
P178995
|
FINISHED |
| Object | romantic interest |
—
|
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 interest | Statement: [Changez, relationshipTypeWithErica, romantic interest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithErica Context triple: [Changez, relationshipTypeWithErica, romantic interest]
-
A.
relationshipToEricForman
Indicates a specified type of personal or social relationship that an entity has with Eric Forman.
-
B.
relationshipToEva
Indicates a specified type of personal or social relationship that an entity has with Eva.
-
C.
relationshipTypeWithEunice
Indicates the specific nature or category of the relationship that an entity has with Eunice.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
relationshipToNicole
Indicates the specific type of relationship or connection that an entity has with Nicole.
- 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_69e75a88fdf081908e47ae6e195c14e1 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f719cc31ec819099bebcf833b14d76 |
completed | May 3, 2026, 9:47 a.m. |
| PD | Predicate disambiguation | batch_69f71820c6c88190ab38b4fa626d22cc |
completed | May 3, 2026, 9:40 a.m. |
| PDg | Predicate description generation | batch_69f71995853c8190912025c0e83640c8 |
completed | May 3, 2026, 9:47 a.m. |
Created at: April 21, 2026, 12:35 p.m.