Triple
T14432900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kolombangara |
E357878
|
entity |
| Predicate | hasNearbyIsland |
P970
|
FINISHED |
| Object | Gizo |
E534100
|
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: Gizo | Statement: [Kolombangara, hasNearbyIsland, Gizo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gizo Context triple: [Kolombangara, hasNearbyIsland, Gizo]
-
A.
Gizo
chosen
Gizo is a small island town in the Solomon Islands known as an administrative and commercial hub in the western part of the country and a popular base for diving and marine tourism.
-
B.
Gugino
Gugino is the surname of American actress Carla Gugino, known for her versatile roles in film and television.
-
C.
Goba
Goba is a small Ethiopian town in the Oromia Region that serves as a primary gateway and service center for visitors to Bale Mountains National Park.
-
D.
Giyera
Giyera is a telekinetic Inhuman and former HYDRA operative in the Marvel Cinematic Universe who serves as a loyal enforcer to the ancient Inhuman entity Hive.
-
E.
Girga
Girga is an ancient town in Upper Egypt, historically significant as a regional center along the Nile.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91471a648190adb7b283a6a85c3e |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bd3e6c48190b4fc3794202a0c3f |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:18 a.m.