Triple
T21413185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Itawit-Tawit |
E528228
|
entity |
| Predicate | isSpokenInProvince |
P7445
|
FINISHED |
| Object | Isabela |
—
|
NE NERFINISHED |
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: Isabela | Statement: [Itawit-Tawit, isSpokenInProvince, Isabela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabela Context triple: [Itawit-Tawit, isSpokenInProvince, Isabela]
-
A.
Isabela
Isabela is a witty, roguish pirate captain and potential companion character in the role-playing video game Dragon Age II.
-
B.
Isabela
chosen
Isabela is a large agricultural province in the Cagayan Valley region of the Philippines, known especially for its extensive rice and corn production.
-
C.
Isabela
Isabela is a coastal municipality in northwestern Puerto Rico known for its beaches, surfing spots, and scenic Atlantic shoreline.
-
D.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
E.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b20017d8819096b1a679edc8943a |
completed | April 22, 2026, 11:33 a.m. |
Created at: April 16, 2026, 5:44 p.m.