Triple
T8964387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayog |
E214089
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Calabarzon |
E97442
|
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: Calabarzon | Statement: [Bayog, partOf, Calabarzon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calabarzon Context triple: [Bayog, partOf, Calabarzon]
-
A.
Calabarzon
chosen
Calabarzon is a populous and industrialized region in the southern part of Luzon in the Philippines, known for its mix of urban centers, agricultural areas, and manufacturing hubs.
-
B.
Aguiguan
Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
-
C.
Ibanag
Ibanag is an Austronesian language spoken primarily in the Cagayan Valley region of northern Luzon in the Philippines.
-
D.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
E.
Gumaca
Gumaca is a coastal municipality in the province of Quezon in the Philippines, known for its historic churches and role as a local commercial center.
- 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_69ca839cd6008190a1546a701a56710c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc674c4be8819090d46aba8ab40af3 |
completed | April 1, 2026, 12:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0470dd24c8190851f71104c9fdf0f |
completed | April 3, 2026, 11:02 p.m. |
Created at: March 30, 2026, 7:01 p.m.