Triple
T15894074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Molendinar |
E385404
|
entity |
| Predicate | hasNeighbouringSuburb |
P41355
|
FINISHED |
| Object | Gaven |
E357321
|
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: Gaven | Statement: [Molendinar, hasNeighbouringSuburb, Gaven]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gaven Context triple: [Molendinar, hasNeighbouringSuburb, Gaven]
-
A.
Gaven
chosen
Gaven is a suburb on the Gold Coast in Queensland, Australia, known for its semi-rural character and proximity to major transport routes.
-
B.
Gavin
Gavin is a masculine given name of Celtic origin, commonly used in English-speaking countries.
-
C.
Garron
Garron is a charming yet unscrupulous conman from the Doctor Who serial "The Ribos Operation," known for orchestrating elaborate planetary fraud schemes.
-
D.
Gavan
Gavan is a given name, typically a variant spelling of Gavin, used as a masculine first name in English-speaking contexts.
-
E.
Garrett
Garrett is a masculine given name of Old French and Germanic origin, commonly used in English-speaking countries.
- 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_69d86da5b800819083a31be937d738b0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1563809748190a54156b946d3f061 |
completed | April 16, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb0497cb481908e8ea4ebb9c4039d |
completed | May 9, 2026, 10:08 p.m. |
Created at: April 10, 2026, 4:51 a.m.