Triple
T10415467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richmond Theatre fire |
E245502
|
entity |
| Predicate | deadliestStatus |
P30647
|
FINISHED |
| Object | one of the deadliest urban fires in early American history |
—
|
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: one of the deadliest urban fires in early American history | Statement: [Richmond Theatre fire, deadliestStatus, one of the deadliest urban fires in early American history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deadliestStatus Context triple: [Richmond Theatre fire, deadliestStatus, one of the deadliest urban fires in early American history]
-
A.
deathStatus
Indicates whether an entity is alive, dead, or in another specified state related to mortality.
-
B.
deadliestIn
chosen
Indicates that something has the highest lethality or causes the most deaths within a specified context, group, or location.
-
C.
mortalStatus
Indicates the state or condition of an entity with respect to being mortal, immortal, or otherwise subject to death.
-
D.
deathOutcome
Indicates that an event, condition, or action results in the death of the affected entity.
-
E.
deathApprox
Indicates that an entity’s death occurred at an approximate, rather than exact, time or date.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea0f7d188190a60a7c3832c51515 |
completed | April 7, 2026, 11:27 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb6f160819090040644a12395ec |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:10 p.m.