Triple
T1722596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hartford Steam Boiler |
E37423
|
entity |
| Predicate | riskArea |
P15871
|
FINISHED |
| Object | mechanical breakdown |
—
|
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: mechanical breakdown | Statement: [Hartford Steam Boiler, riskArea, mechanical breakdown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riskArea Context triple: [Hartford Steam Boiler, riskArea, mechanical breakdown]
-
A.
riskType
chosen
Indicates the category or nature of risk associated with an entity, event, or relationship.
-
B.
riskGroup
Indicates that an entity belongs to a category of individuals or items that share an elevated level of risk relative to others.
-
C.
risk
Indicates that one entity is exposed or subject to potential harm, loss, or adverse outcome arising from another entity, action, or situation.
-
D.
riskFeature
Indicates that one entity possesses or exhibits a characteristic, condition, or attribute that increases the likelihood or severity of a negative outcome for another entity or situation.
-
E.
riskAddressed
Indicates that a particular risk has been identified and is being mitigated, managed, or otherwise handled by an associated action, control, or measure.
- 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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aadb7bda1081908f2c41c520c9c55c |
completed | March 6, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69aa61c0a0288190bce9d60062a84b69 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.