Triple
T20406704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorine |
E500484
|
entity |
| Predicate | relationshipToMariane |
P140029
|
FINISHED |
| Object | maid and confidante of Mariane |
—
|
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: maid and confidante of Mariane | Statement: [Dorine, relationshipToMariane, maid and confidante of Mariane]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMariane Context triple: [Dorine, relationshipToMariane, maid and confidante of Mariane]
-
A.
relationshipToMary
Indicates that one entity stands in a specified personal or social relationship to Mary.
-
B.
relationshipToMarcy
Indicates that one entity has a specified personal or social relationship to Marcy.
-
C.
relationshipWithMarcello
Indicates that there exists some form of relationship or connection between an entity and Marcello.
-
D.
relationshipToMadameMerle
Indicates the specific nature or type of relationship an entity has with Madame Merle.
-
E.
relationshipToMichelle
Indicates the specific type of relationship or connection that an entity has to Michelle.
- 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_69e0b4a81bec8190b69adfdc1336a015 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67993dc7081908ebd54ec92e712ea |
completed | April 20, 2026, 7:08 p.m. |
| PD | Predicate disambiguation | batch_69e5765d7cb48190adec18d6d1e3d263 |
completed | April 20, 2026, 12:42 a.m. |
| PDg | Predicate description generation | batch_69e58d7481508190a87c8b88f9df9879 |
completed | April 20, 2026, 2:20 a.m. |
Created at: April 16, 2026, 11:29 a.m.