Triple
T5647647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colusa County |
E124422
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Williams |
E81905
|
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: Williams | Statement: [Colusa County, hasCity, Williams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Williams Context triple: [Colusa County, hasCity, Williams]
-
A.
Williams
chosen
Williams is a common English surname borne by numerous notable figures across sports, politics, arts, and entertainment.
-
B.
Wilson
"Wilson" is a 1944 American biographical film about U.S. President Woodrow Wilson, noted for its ambitious production and multiple Academy Awards.
-
C.
Wilson
Wilson is a common English-language surname borne by numerous notable figures across fields such as science, politics, sports, and the arts.
-
D.
Wilson
Wilson is a Chicago Transit Authority 'L' station on the North Side that serves as a major stop on the Red Line.
-
E.
Johnson
Johnson is a common English surname borne by numerous notable individuals across politics, arts, sports, and other fields.
- 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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022d1534c8190ac4828e44300fb91 |
completed | March 22, 2026, 5:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a218c688190879462d507f94b36 |
completed | March 22, 2026, 9:07 p.m. |
Created at: March 22, 2026, 3:42 p.m.