Triple
T31537215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burke, Vermont |
E804638
|
entity |
| Predicate | hasEducationalInstitution |
P113
|
FINISHED |
| Object |
Burke Town School
Burke Town School is a local public school serving students in the rural community of Burke, Vermont.
|
E1968107
|
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: Burke Town School | Statement: [Burke, Vermont, hasEducationalInstitution, Burke Town School]
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: Burke Town School Triple: [Burke, Vermont, hasEducationalInstitution, Burke Town School]
Generated description
Burke Town School is a local public school serving students in the rural community of Burke, Vermont.
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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a78300c0819099abfce061b000c9 |
completed | May 3, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2b2d84968c81909b6ade2c73789fa6 |
completed | June 11, 2026, 9:49 p.m. |
| NEDg | Description generation | batch_6a2b2ffcc2cc819098c4aaea39a139b1 |
completed | June 11, 2026, 10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2b3360e01881909c071707b37e0e70 |
completed | June 11, 2026, 10:14 p.m. |
Created at: April 30, 2026, 10:04 p.m.