Triple
T7802444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muskrat Falls Generating Station |
E180461
|
entity |
| Predicate | hasFinancialIssue |
P24789
|
FINISHED |
| Object | cost overruns |
—
|
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: cost overruns | Statement: [Muskrat Falls Generating Station, hasFinancialIssue, cost overruns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFinancialIssue Context triple: [Muskrat Falls Generating Station, hasFinancialIssue, cost overruns]
-
A.
hasEconomicChallenge
chosen
Indicates that an entity is experiencing or facing a financial or economic difficulty, constraint, or problem.
-
B.
hasSocioeconomicIssue
Indicates that an entity is affected by, associated with, or involved in a socioeconomic problem or challenge.
-
C.
facingIssue
Indicates that an entity is currently experiencing, encountering, or dealing with a problem, difficulty, or obstacle.
-
D.
hasCredit
Indicates that an entity possesses or is assigned a credit, such as financial credit, academic credit, or acknowledgment for a contribution.
-
E.
hasLegalIssue
Indicates that an entity is involved in, associated with, or subject to a legal problem, dispute, or proceeding.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:33 p.m.