Triple
T5996499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nate Silver |
E133481
|
entity |
| Predicate | hasTwitterAccount |
P57
|
FINISHED |
| Object | @NateSilver538 |
E133481
|
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: @NateSilver538 | Statement: [Nate Silver, hasTwitterAccount, @NateSilver538]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: @NateSilver538 Context triple: [Nate Silver, hasTwitterAccount, @NateSilver538]
-
A.
Nate Silver
chosen
Nate Silver is an American statistician and writer best known for founding the data-driven news site FiveThirtyEight and for his influential election forecasting.
-
B.
Nate
Nate is a central fictional character in Margaret Atwood’s novel "Life Before Man," around whom much of the story’s emotional and relational tension revolves.
-
C.
Nate
Nate is a common diminutive form of the given name Nathaniel, often used as a casual or familiar nickname.
-
D.
Nate
Nate is the Allied reporting name for the Nakajima Ki-27, a Japanese single-engine fighter aircraft used extensively by the Imperial Japanese Army Air Service in the late 1930s and early World War II.
-
E.
Matthew Gentzkow
Matthew Gentzkow is an American economist known for his influential research on media, political communication, and industrial organization.
- 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_69c00870ddbc81909880fa3864f4f38d |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04e963f3c819082dd755e328ab947 |
completed | March 22, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c10876d4d0819083ac7431c8abaedd |
completed | March 23, 2026, 9:31 a.m. |
Created at: March 22, 2026, 4:05 p.m.