Triple
T7560207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topa |
E178773
|
entity |
| Predicate | relationshipWithKlyden |
P77615
|
FINISHED |
| Object | strained after transition |
—
|
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: strained after transition | Statement: [Topa, relationshipWithKlyden, strained after transition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithKlyden Context triple: [Topa, relationshipWithKlyden, strained after transition]
-
A.
relationshipToKateKeller
Indicates the specific familial, social, or interpersonal connection that one entity has to Kate Keller.
-
B.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
-
C.
relationshipToLarryKeller
Indicates the specific type of relationship or connection an entity has to Larry Keller.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
relationshipToChrisKeller
Indicates the specific type of personal or social relationship an entity has with Chris Keller.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8dd96488190b4cca25ae8f7f95c |
completed | March 27, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69c6f4dc485c819080da13e3b7f4f08f |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f59517648190ac0a9e9cba045dd5 |
completed | March 27, 2026, 9:24 p.m. |
Created at: March 27, 2026, 3:50 p.m.