Triple
T17907917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankie Foster |
E447748
|
entity |
| Predicate | relationshipToMadameFoster |
P129254
|
FINISHED |
| Object | granddaughter |
—
|
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: granddaughter | Statement: [Frankie Foster, relationshipToMadameFoster, granddaughter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMadameFoster Context triple: [Frankie Foster, relationshipToMadameFoster, granddaughter]
-
A.
relationshipToMaisie
Indicates the specific type of personal or social relationship that an entity has with Maisie.
-
B.
relationshipToMadameMerle
Indicates the specific nature or type of relationship an entity has with Madame Merle.
-
C.
relationshipToOdette
Indicates the specific familial, social, or interpersonal connection that an entity has with Odette.
-
D.
relationshipToBaudelaires
Indicates the type of personal or familial connection an entity has to the Baudelaires.
-
E.
relationshipToMother
Indicates the specific familial or social connection an entity has to its mother.
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49e9d458881909e35e1c7a6e85436 |
completed | April 19, 2026, 9:21 a.m. |
| PD | Predicate disambiguation | batch_69e3d8ec2f6881909d7f54b878cbed37 |
completed | April 18, 2026, 7:18 p.m. |
| PDg | Predicate description generation | batch_69e3db77df0c819084548168c62b398c |
completed | April 18, 2026, 7:28 p.m. |
Created at: April 10, 2026, 10:19 a.m.