Triple
T12577158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burnet County |
E300237
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Burnet |
E990582
|
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: Burnet | Statement: [Burnet County, hasCity, Burnet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burnet Context triple: [Burnet County, hasCity, Burnet]
-
A.
Burnet
Burnet is the middle name of William Burnet Tuthill, the American architect best known for designing New York’s Carnegie Hall.
-
B.
Burnet
chosen
Burnet is a small central Texas city that serves as the administrative and commercial hub of Burnet County.
-
C.
Burnett
Burnett is a surname most famously associated with American comedian and actress Carol Burnett, a pioneering figure in television sketch comedy.
-
D.
Burkley
Burkley is a surname most notably associated with American character actor Dennis Burkley.
-
E.
Brewster
Brewster is a coastal town on Cape Cod in Massachusetts known for its scenic beaches, historic charm, and bayside conservation lands.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954a73c148190bba8f16b1232fd46 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ebab65081908a174586f0ebb16f |
completed | May 2, 2026, 8:29 p.m. |
Created at: April 9, 2026, 4:53 p.m.