Triple
T70800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Francisco cable car system |
E1416
|
entity |
| Predicate | safetyIncidentHistory |
P2107
|
FINISHED |
| Object | has experienced accidents and safety concerns over time |
—
|
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: has experienced accidents and safety concerns over time | Statement: [San Francisco cable car system, safetyIncidentHistory, has experienced accidents and safety concerns over time]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyIncidentHistory Context triple: [San Francisco cable car system, safetyIncidentHistory, has experienced accidents and safety concerns over time]
-
A.
accidentType
Indicates the specific category or kind of accident associated with an event or incident.
-
B.
hazardType
Indicates the specific kind or category of hazard associated with an entity or situation.
-
C.
historicalClaim
Indicates that an entity asserts or presents a statement about past events or conditions as a matter of historical fact.
-
D.
hasWaterQualityHistory
Indicates that an entity is associated with a record or series of records describing changes or measurements of its water quality over time.
-
E.
hasHistoricalEvent
chosen
Indicates that a historical event occurred in, is associated with, or is relevant to a particular entity.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24eaa0df88190add55579b2b9fd02 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.