Triple
T17351901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katherine Airport |
E421832
|
entity |
| Predicate | hasCityServed |
P3936
|
FINISHED |
| Object | Katherine |
—
|
NE NERFINISHED |
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: Katherine | Statement: [Katherine Airport, hasCityServed, Katherine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katherine Context triple: [Katherine Airport, hasCityServed, Katherine]
-
A.
Katherine
Katherine is the central protagonist of the story "The Well," around whom the narrative’s main events and conflicts revolve.
-
B.
Katherine
Katherine is a feminine given name of Greek origin, commonly associated with meanings related to purity.
-
C.
Katherine
chosen
Katherine is a regional town in Australia's Northern Territory, known as a key service and transport hub near Nitmiluk (Katherine Gorge) National Park.
-
D.
Katherine
Katherine is one of the witty noblewomen in William Shakespeare’s comedy "Love’s Labour’s Lost," known for her sharp dialogue and role in the play’s romantic entanglements.
-
E.
Katherine
Katherine is the daughter of Evelyn Mulwray in the 1974 film noir "Chinatown," whose parentage is central to the movie's mystery and emotional impact.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a2dae648190b7f3487919a446af |
completed | April 19, 2026, 2:13 a.m. |
Created at: April 10, 2026, 5:44 a.m.