Triple
T19399023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silas |
E485267
|
entity |
| Predicate | relationshipTo |
P37
|
FINISHED |
| Object | Warren |
—
|
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: Warren | Statement: [Silas, relationshipTo, Warren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warren Context triple: [Silas, relationshipTo, Warren]
-
A.
Warren
Warren is a mid-sized industrial city in northeastern Ohio known historically for its role in the steel and automotive industries.
-
B.
Warren
Warren "Baby" Dodds was an influential early jazz drummer known for his pioneering work in New Orleans and Chicago jazz.
-
C.
Warren
Warren is the given name of Warren Buffett, the renowned American investor and longtime CEO of Berkshire Hathaway.
-
D.
Warren
Warren is a common English surname borne by numerous notable figures in politics, law, entertainment, and other fields.
-
E.
Warren
Warren is a large suburban city in southeast Michigan known for its extensive automotive and defense manufacturing industries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62575d78881909c2cfee799859261 |
completed | April 20, 2026, 1:09 p.m. |
Created at: April 10, 2026, 1:36 p.m.