Triple
T10601744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis, Duke of Burgundy (1682–1712) |
E275763
|
entity |
| Predicate | impactOfDeath |
P27814
|
FINISHED |
| Object | led to succession of his son Louis XV |
—
|
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: led to succession of his son Louis XV | Statement: [Louis, Duke of Burgundy (1682–1712), impactOfDeath, led to succession of his son Louis XV]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOfDeath Context triple: [Louis, Duke of Burgundy (1682–1712), impactOfDeath, led to succession of his son Louis XV]
-
A.
effectOfDeath
chosen
Indicates the causal impact or consequences that a death has on another entity, state, or process.
-
B.
impactOutcome
Indicates that one entity produces an effect or influence that changes the result, consequence, or final state of another entity or situation.
-
C.
deathContributedTo
Indicates that one entity played a causal or contributing role in bringing about the death of another entity.
-
D.
casualtiesImpact
Indicates how the number or severity of casualties affects or influences another factor, situation, or outcome.
-
E.
deathIs
Indicates that one entity is the cause, manner, or circumstance of another entity’s death.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4992248190b640d743ccf02c82 |
completed | April 8, 2026, 11:05 p.m. |
| PD | Predicate disambiguation | batch_69d6dd72c1288190adbb5e79e94c044a |
completed | April 8, 2026, 10:57 p.m. |
Created at: April 8, 2026, 7:31 p.m.