Triple
T28598510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ironworkers Memorial Second Narrows Crossing |
E723841
|
entity |
| Predicate | additionalDeathsRelatedToAccident |
P164896
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Ironworkers Memorial Second Narrows Crossing, additionalDeathsRelatedToAccident, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: additionalDeathsRelatedToAccident Context triple: [Ironworkers Memorial Second Narrows Crossing, additionalDeathsRelatedToAccident, 4]
-
A.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
B.
fatalAccident
Indicates that an accident resulted in at least one death.
-
C.
resultOfAccident
Indicates that something exists or occurs as a consequence or outcome of an accident.
-
D.
numberOfFatalitiesInvestigatedAccident
Indicates the count of deaths that were examined as part of the investigation into a specific accident.
-
E.
constructionAccidentFatalities
Indicates that a construction-related accident resulted in one or more fatalities.
- 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_69f01d80b1908190980594837604b8c7 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651aad92c8190b874b3b5f9f64434 |
completed | May 2, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69f652a3a8208190a4fdd66f34138bd2 |
completed | May 2, 2026, 7:38 p.m. |
Created at: April 28, 2026, 4:23 a.m.