Triple
T6373023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dow Jones Transportation Average |
E143395
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Edward Jones |
E97813
|
NE 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: Edward Jones | Statement: [Dow Jones Transportation Average, creator, Edward Jones]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Edward Jones Context triple: [Dow Jones Transportation Average, creator, Edward Jones]
-
A.
Edward Jones
chosen
Edward Jones was an American statistician and co-founder of Dow Jones & Company, best known for helping create the Dow Jones Industrial Average.
-
B.
Charles R. Schwab
Charles R. Schwab is an American investor and businessman best known as the founder of the Charles Schwab Corporation, a pioneering discount brokerage firm.
-
C.
Sidney Schwab
Sidney Schwab is an individual notable enough to be recognized as a prominent bearer of the Schwab surname.
-
D.
John C. Schwab
John C. Schwab was an American economist and librarian best known for serving as the librarian of Yale University in the late 19th and early 20th centuries.
-
E.
Martin Schwab
Martin Schwab is a German actor known for his extensive work in theater, film, and television.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06829d76c819092b476631459233a |
completed | March 22, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d9203988190a535b4f06f478292 |
completed | March 27, 2026, 7:11 a.m. |
Created at: March 22, 2026, 4:33 p.m.