Triple
T35718246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fuhrmann Henschel |
E1032093
|
entity |
| Predicate | hasTragicOutcome |
P21136
|
FINISHED |
| Object | fate of wagon driver is tragic |
—
|
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: fate of wagon driver is tragic | Statement: [Fuhrmann Henschel, hasTragicOutcome, fate of wagon driver is tragic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTragicOutcome Context triple: [Fuhrmann Henschel, hasTragicOutcome, fate of wagon driver is tragic]
-
A.
hasTragicEnding
chosen
Indicates that the event, story, or situation concludes with a sorrowful, disastrous, or otherwise deeply unfortunate outcome.
-
B.
hasTragicPast
Indicates that an entity has experienced a significantly sorrowful or traumatic history that influences its present state or characterization.
-
C.
deathOutcome
Indicates that an event, condition, or action results in the death of the affected entity.
-
D.
deathResultedIn
Indicates that one event, action, or condition caused or led directly to a particular death as its outcome.
-
E.
hasMannerOfDeath
Indicates the specific way or circumstances in which an entity died, such as natural causes, accident, homicide, or suicide.
- 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_69f76e0df1d08190965b1c6dff94c391 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ffc605b0648190a7abe9128b0d857a |
completed | May 9, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69ffc5742d80819099f947ece78d5700 |
completed | May 9, 2026, 11:38 p.m. |
Created at: May 3, 2026, 4:05 p.m.