Triple
T23871269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bagne of Toulon |
E592735
|
entity |
| Predicate | fictionalInmate |
P48975
|
FINISHED |
| Object | Jean Valjean |
—
|
NE NERFINISHED |
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: Jean Valjean | Statement: [Bagne of Toulon, fictionalInmate, Jean Valjean]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalInmate Context triple: [Bagne of Toulon, fictionalInmate, Jean Valjean]
-
A.
fictionalPrisoner
chosen
Indicates that an entity is portrayed as a prisoner within a fictional or narrative context.
-
B.
fictionalPrisonName
Indicates that an entity is identified by the name of a fictional prison.
-
C.
fictionalSon
Indicates that one entity is portrayed as the son of another entity within a fictional or narrative context.
-
D.
fictionalizationOf
Indicates that one entity is a fictional or dramatized representation, adaptation, or reimagining of another (typically real or earlier) entity or event.
-
E.
fictionalAuthorVictim
Indicates that one entity is the author of a fictional work in which the other entity appears as a victim.
- 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_69e25d23a5c88190ae3999c70ca15e08 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1cbfe1f948190ad2c0d5bbe0c7240 |
completed | April 29, 2026, 9:14 a.m. |
| PD | Predicate disambiguation | batch_69f1614a65a88190bde1efb368a151e4 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:14 p.m.