Triple
T3199628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippine languages |
E67018
|
entity |
| Predicate | includesLanguage |
P2177
|
FINISHED |
| Object | Ibanag |
E101548
|
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: Ibanag | Statement: [Philippine languages, includesLanguage, Ibanag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ibanag Context triple: [Philippine languages, includesLanguage, Ibanag]
-
A.
Ibanag
chosen
Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
-
B.
Kapampangan
Kapampangan is an Austronesian language spoken primarily in the Pampanga region of the Philippines by the Kapampangan ethnic group.
-
C.
Aguiguan
Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
-
D.
Yakan
Yakan is an Austronesian language spoken primarily by the Yakan people of Basilan and nearby areas in the southern Philippines.
-
E.
Bukidnon
Bukidnon is a landlocked, mountainous province in the Philippines known for its vast agricultural plantations, cool climate, and scenic highland landscapes.
- 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_69ad8589bd988190afa7ed2bdffb7b33 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada9ad4b1c8190bc6ad0f025f238c8 |
completed | March 8, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2621d3bcc8190abf84310bc118757 |
completed | March 12, 2026, 6:50 a.m. |
Created at: March 8, 2026, 3:07 p.m.