Triple
T6675769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Tarawa |
E151847
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Taborio |
E438950
|
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: Taborio | Statement: [South Tarawa, hasPart, Taborio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taborio Context triple: [South Tarawa, hasPart, Taborio]
-
A.
Taborio
chosen
Taborio is a village settlement located on the island of Nonouti in the Republic of Kiribati.
-
B.
Tarusa
Tarusa is a small historic town in western Russia known for its scenic location on the Oka River and its associations with Russian artists and writers.
-
C.
Krempna
Krempna is a small village in southeastern Poland that serves as a gateway and service center for visitors to Magura National Park in the Low Beskid Mountains.
-
D.
Trebsen
Trebsen is a small town in the Free State of Saxony in eastern Germany, known for its historic castle and location along the Mulde River.
-
E.
Oreshek
Oreshek is the historic Russian fortress on Lake Ladoga that later gave rise to the town of Shlisselburg.
- 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_69c687f830bc81909eb8b04dbb8450b1 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b0f3021481908c2599349eb6ea07 |
completed | March 27, 2026, 4:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f7a30b7481908c36ff9035f62731 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:03 p.m.