Triple
T5224719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dixon |
E117956
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Dixon |
E117956
|
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: Dixon | Statement: [Dixon, hasName, Dixon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dixon Context triple: [Dixon, hasName, Dixon]
-
A.
Dixon
chosen
Dixon is a small agricultural city in Northern California known historically for sheep ranching and its annual May Fair.
-
B.
Dillon
Dillon is the middle name of famed American baseball player and manager Casey Stengel.
-
C.
Dillon
Dillon is a surname of Irish origin that has been borne by numerous notable individuals across various fields.
-
D.
Dillon
Dillon is a small city in southwestern Montana known as a regional hub for ranching, outdoor recreation, and as the home of the University of Montana Western.
-
E.
Dillon
Dillon is a tough, muscular CIA operative portrayed by Carl Weathers in the 1987 sci-fi action film "Predator."
- 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_69bd4465e03081909bfcfd7113062590 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7abd3ed48190bfd8d2f2ca399741 |
completed | March 20, 2026, 4:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69befe5e0d4c819095ea8b185754394e |
completed | March 21, 2026, 8:23 p.m. |
Created at: March 20, 2026, 1:48 p.m.