Triple
T28469647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clive Durham |
E720397
|
entity |
| Predicate | relationshipWithMaurice |
P201577
|
FINISHED |
| Object | close friendship |
—
|
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: close friendship | Statement: [Clive Durham, relationshipWithMaurice, close friendship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithMaurice Context triple: [Clive Durham, relationshipWithMaurice, close friendship]
-
A.
relationshipToMadameMerle
Indicates the specific nature or type of relationship an entity has with Madame Merle.
-
B.
relationshipToMariane
Indicates the specific type of relationship or connection that an entity has to Mariane.
-
C.
relationToVonMaur
Indicates a specified type of relationship or association that an entity has with Von Maur.
-
D.
relationshipWithMarcello
Indicates that there exists some form of relationship or connection between an entity and Marcello.
-
E.
relationshipToMariusPontmercy
Indicates the specific type of personal or social relationship an entity has with Marius Pontmercy.
- 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_69f01a58a67c819097936d9e8da8d6e6 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_6a0008225cc081909ff1fd0639859dc4 |
completed | May 10, 2026, 4:22 a.m. |
| PD | Predicate disambiguation | batch_6a0007bac5d8819098aff8031d4abe5d |
completed | May 10, 2026, 4:21 a.m. |
| PDg | Predicate description generation | batch_6a000821b12c819093ca3b3dc0cff370 |
completed | May 10, 2026, 4:22 a.m. |
Created at: April 28, 2026, 2:47 a.m.