Triple
T13891061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capul Island lighthouse |
E333969
|
entity |
| Predicate | languageOfLocality |
P10892
|
FINISHED |
| Object | Inabaknon |
E1021047
|
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: Inabaknon | Statement: [Capul Island lighthouse, languageOfLocality, Inabaknon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inabaknon Context triple: [Capul Island lighthouse, languageOfLocality, Inabaknon]
-
A.
Inabaknon
chosen
Inabaknon is an Austronesian language spoken primarily on Capul Island in Northern Samar, Philippines, known for its distinctiveness from the surrounding Visayan languages.
-
B.
Nabawan
Nabawan is a rural town and district in the interior of Sabah, Malaysia, known for its indigenous communities and agricultural activities.
-
C.
Kabugao
Kabugao is a dialect of the Isnag language spoken by indigenous communities in the northern Philippines.
-
D.
Kayabacho
Kayabacho is a commercial district in Tokyo's Chūō ward known as a financial hub with dense office buildings and convenient subway access.
-
E.
Babatngon
Babatngon is a coastal municipality in the province of Leyte in the Philippines, known for its fishing industry and rural communities.
- 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_69d81c5dd2d48190b7a5fc1e009de936 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a3a24881908d81d634622fbbcc |
completed | April 14, 2026, 11:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c71a43908190bc7537f0a2379599 |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:15 p.m.