Triple
T3612222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Cargelligo |
E76514
|
entity |
| Predicate | distanceFromGriffith |
P44559
|
FINISHED |
| Object | approximately 150 km north |
—
|
LITERAL 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: approximately 150 km north | Statement: [Lake Cargelligo, distanceFromGriffith, approximately 150 km north]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromGriffith Context triple: [Lake Cargelligo, distanceFromGriffith, approximately 150 km north]
-
A.
distanceToGriffith
chosen
Indicates the measured or calculated distance between a given entity and Griffith.
-
B.
distanceFromSanFrancisco
Indicates the measured distance between a given entity’s location and the city of San Francisco.
-
C.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
D.
distanceFromGeelong
Indicates the spatial distance between a given entity or location and Geelong.
-
E.
distanceFromGrandCentral
Indicates the spatial distance between a given entity and Grand Central.
- F. None of above.
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_69ad85da0ba481908b3b48c69efe2b98 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc22e5b30819084184b730732c727 |
completed | March 8, 2026, 6:38 p.m. |
| PD | Predicate disambiguation | batch_69adb83d8b1c8190b3bddbc5dc995a87 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.