Triple
T18111564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Labasa Airport |
E433486
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Labasa |
—
|
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: Labasa | Statement: [Labasa Airport, cityServed, Labasa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Labasa Context triple: [Labasa Airport, cityServed, Labasa]
-
A.
Labasa
chosen
Labasa is a major town in northern Fiji on the island of Vanua Levu, known as an important commercial and administrative center for the surrounding sugarcane-growing region.
-
B.
Palolo
Palolo is a residential valley neighborhood in urban Honolulu on the island of Oahu, Hawaii.
-
C.
Sarilamak
Sarilamak is a town in West Sumatra, Indonesia, that serves as the administrative center of Lima Puluh Kota Regency.
-
D.
Calabarzon
Calabarzon is a populous and industrialized region in the southern part of Luzon in the Philippines, known for its mix of urban centers, agricultural areas, and manufacturing hubs.
-
E.
Sarigan
Sarigan is a small, uninhabited volcanic island in the Northern Mariana Islands known for its active stratovolcano and protected wildlife habitats.
- 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_69d8b90916008190a1f110bd7ced5473 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd3422c81908396a21bd53f3e47 |
completed | April 19, 2026, 1:51 p.m. |
Created at: April 10, 2026, 10:28 a.m.