Triple
T4899069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Wing fire of 1929 |
E109751
|
entity |
| Predicate | hadNoFatalities |
P60545
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [West Wing fire of 1929, hadNoFatalities, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadNoFatalities Context triple: [West Wing fire of 1929, hadNoFatalities, true]
-
A.
deathWithoutIssue
Indicates that a person has died without leaving any surviving descendants or heirs.
-
B.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
C.
hasNotableIncident
Indicates that an entity is associated with a significant or noteworthy event, occurrence, or incident.
-
D.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
E.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
- 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_69bd4410bbf88190aad50d2451c863d6 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd706245e48190a61d573438461c30 |
completed | March 20, 2026, 4:05 p.m. |
| PD | Predicate disambiguation | batch_69bd6c306b188190a08a7856beb76db4 |
completed | March 20, 2026, 3:48 p.m. |
| PDg | Predicate description generation | batch_69bd7060f9988190afdf98eb0a38515d |
completed | March 20, 2026, 4:05 p.m. |
Created at: March 20, 2026, 1:28 p.m.