Triple
T37121576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roskilde Festival |
E919269
|
entity |
| Predicate | numberOfFatalitiesIn2000Accident |
P205739
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Roskilde Festival, numberOfFatalitiesIn2000Accident, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFatalitiesIn2000Accident Context triple: [Roskilde Festival, numberOfFatalitiesIn2000Accident, 9]
-
A.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
B.
numberOfFatalitiesInvestigatedAccident
Indicates the count of deaths that were examined as part of the investigation into a specific accident.
-
C.
additionalDeathsRelatedToAccident
Indicates that there were extra fatalities occurring as a consequence of the accident beyond any initially recorded or primary deaths.
-
D.
numberOfBombsInAccident
Indicates the quantity of bombs involved in a specific accident.
-
E.
numberOfUSFatalities
Indicates the number of people who died in the United States as a result of the specified event or circumstance.
- 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_69f76e9c57148190ba789dd059645bb9 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c842b2c819082f1d2db995ac2eb |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:15 p.m.