Triple
T20912001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephanie Flanders |
E514970
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Financial Times |
—
|
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: Financial Times | Statement: [Stephanie Flanders, employer, Financial Times]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Financial Times Context triple: [Stephanie Flanders, employer, Financial Times]
-
A.
Financial Times
chosen
The Financial Times is a leading international daily newspaper based in London, renowned for its global business, economic, and financial news coverage.
-
B.
The Economist
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.
-
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.
WSJ
WSJ is the Indian Railways station code for Wansjaliya Junction railway station in Gujarat, India.
-
E.
The Sunday Times
The Sunday Times is a prominent British Sunday newspaper known for its in-depth journalism, investigative reporting, and influential commentary.
- 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_69e0b4f9d5ec8190bb2bd27350ed341c |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6ec5f5bc48190a8e7e6af60b158fb |
completed | April 21, 2026, 3:17 a.m. |
Created at: April 16, 2026, 12:48 p.m.