Triple
T2851331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maloelap Atoll |
E63097
|
entity |
| Predicate | hasIsland |
P970
|
FINISHED |
| Object | Taroa |
E303131
|
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: Taroa | Statement: [Maloelap Atoll, hasIsland, Taroa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taroa Context triple: [Maloelap Atoll, hasIsland, Taroa]
-
A.
Taroa
chosen
Taroa is the main settlement and administrative center of Maloelap Atoll in the Marshall Islands, known historically for its World War II-era Japanese airbase.
-
B.
Tayassu
Tayassu is a genus of New World peccaries, medium-sized pig-like mammals native to Central and South American forests and scrublands.
-
C.
Gressier
Gressier is a coastal commune in western Haiti known for its proximity to Port-au-Prince and its vulnerability to earthquakes and hurricanes.
-
D.
Galarina
Galarina is a famous surrealist painting by Salvador Dalí depicting his muse and wife Gala in a classical, devotional style.
-
E.
Pantar
Pantar is an island in eastern Indonesia’s Alor archipelago, known for its linguistic diversity and use of several Central Malayo-Polynesian languages.
- 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf5ca2648190bd32c6ec4b0dd3b6 |
completed | March 7, 2026, 8:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b01d81e76c8190aae5e6dd9b13e28b |
completed | March 10, 2026, 1:32 p.m. |
Created at: March 6, 2026, 10:02 p.m.