Triple
T18162607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juliette Froissy |
E434803
|
entity |
| Predicate | relativeLackOfBiographicalInformation |
P44752
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Juliette Froissy, relativeLackOfBiographicalInformation, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeLackOfBiographicalInformation Context triple: [Juliette Froissy, relativeLackOfBiographicalInformation, true]
-
A.
hasUncertainBiographicalDetails
chosen
Indicates that the biographical information about an entity is incomplete, ambiguous, or not reliably established.
-
B.
lacksNotabilityAs
Indicates that one entity is considered insufficiently notable or significant to be associated with or classified as the other entity.
-
C.
usesBiographicalStructure
Indicates that one entity employs or is organized according to the biographical structure of another entity (e.g., a work structured around a person’s life story).
-
D.
hasBiographicalStyle
Indicates that something is characterized by or presented in a biographical manner or style.
-
E.
hasPartInBiography
Indicates that a person or entity is featured or plays a role within someone’s biographical account.
- F. None of above.
Provenance (3 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dec419788190a999a68f32fab39b |
completed | April 19, 2026, 1:55 p.m. |
| PD | Predicate disambiguation | batch_69e4331baeb88190b21f50a98c36c78e |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:30 a.m.