Triple
T18878664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Kinsley |
E461756
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | The Economist |
—
|
NE NERFINISHED |
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: The Economist | Statement: [Michael Kinsley, employer, The Economist]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Economist Context triple: [Michael Kinsley, employer, The Economist]
-
A.
The Economist
chosen
The Economist is an international weekly news and business publication known for its in-depth analysis and commentary on global politics, economics, and current affairs.
-
B.
Financial Times
The Financial Times is a leading international daily newspaper based in London, renowned for its global business, economic, and financial news coverage.
-
C.
The Wall Street Journal
The Wall Street Journal is a leading American business-focused daily newspaper known for its influential financial reporting and analysis.
-
D.
The Economist Group
The Economist Group is an international media and information company best known for publishing The Economist magazine and providing analysis on global affairs, business, and economics.
-
E.
WSJ
WSJ is the Indian Railways station code for Wansjaliya Junction railway station in Gujarat, India.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3cfc4408190a7ae91459f75be52 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 10, 2026, 11:57 a.m.