Triple
T12081395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Sloper |
E287685
|
entity |
| Predicate | relationshipWithMorrisTownsend |
P103100
|
FINISHED |
| Object | romantic but ultimately broken |
—
|
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 but ultimately broken | Statement: [Catherine Sloper, relationshipWithMorrisTownsend, romantic but ultimately broken]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithMorrisTownsend Context triple: [Catherine Sloper, relationshipWithMorrisTownsend, romantic but ultimately broken]
-
A.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
B.
relationshipWithTobyFlenderson
Indicates that one entity has some form of relationship, connection, or association with Toby Flenderson.
-
C.
relationshipToTerry
Indicates the specific type of personal or social relationship that one entity has with Terry.
-
D.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
E.
relationToProvidence
Indicates a relationship or connection that something or someone has to Providence, such as origin, location, affiliation, or relevance.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bf4f508190842927e7e0642235 |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d91006e14081909838412df082f794 |
completed | April 10, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:48 p.m.