Triple
T12497103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Albans, Vermont |
E298719
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Timothy Smith
Timothy Smith is an American local politician serving as the mayor of St. Albans, Vermont.
|
E994233
|
NE FINISHED |
How this triple was built (4 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: Timothy Smith | Statement: [St. Albans, Vermont, hasMayor, Timothy Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Timothy Smith Context triple: [St. Albans, Vermont, hasMayor, Timothy Smith]
-
A.
Timothy Jude Smith
Timothy Jude Smith, better known as Timmy Trumpet, is an Australian DJ, producer, and musician renowned for blending live trumpet performances with electronic dance music.
-
B.
Tim Smith
Tim Smith is a senior British police officer serving as the Chief Constable of Kent Police.
-
C.
Tim Smith
Tim Smith is a former British Conservative Party politician who served as a Member of Parliament in the late 20th century.
-
D.
Mark Smith
Mark Smith is a renowned designer known for his influential work with Nike, including creating iconic basketball-related trophies and products.
-
E.
Nick Smith
Nick Smith is the doomed husband and diner owner whose murder becomes the central plot of the 1946 film noir "The Postman Always Rings Twice."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Timothy Smith Triple: [St. Albans, Vermont, hasMayor, Timothy Smith]
Generated description
Timothy Smith is an American local politician serving as the mayor of St. Albans, Vermont.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Timothy Smith Target entity description: Timothy Smith is an American local politician serving as the mayor of St. Albans, Vermont.
-
A.
Timothy Jude Smith
Timothy Jude Smith, better known as Timmy Trumpet, is an Australian DJ, producer, and musician renowned for blending live trumpet performances with electronic dance music.
-
B.
Tim Smith
Tim Smith is a senior British police officer serving as the Chief Constable of Kent Police.
-
C.
Tim Smith
Tim Smith is a former British Conservative Party politician who served as a Member of Parliament in the late 20th century.
-
D.
Mark Smith
Mark Smith is a renowned designer known for his influential work with Nike, including creating iconic basketball-related trophies and products.
-
E.
Nick Smith
Nick Smith is the doomed husband and diner owner whose murder becomes the central plot of the 1946 film noir "The Postman Always Rings Twice."
- F. None of above. chosen
Provenance (5 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94df948308190ace333230a4a3b38 |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6685bafcc8190beae748d979762e1 |
completed | May 2, 2026, 9:10 p.m. |
| NEDg | Description generation | batch_69f669527fe881909baeb84ccff506c8 |
completed | May 2, 2026, 9:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f669fe4bc48190adba50ad58b10c45 |
completed | May 2, 2026, 9:17 p.m. |
Created at: April 8, 2026, 9:57 p.m.