Triple
T16340993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Jose, Negros Oriental |
E396802
|
entity |
| Predicate | hasBarangay |
P29835
|
FINISHED |
| Object | Tayasan |
E251861
|
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: Tayasan | Statement: [San Jose, Negros Oriental, hasBarangay, Tayasan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tayasan Context triple: [San Jose, Negros Oriental, hasBarangay, Tayasan]
-
A.
Tayasan
chosen
Tayasan is a coastal municipality in the province of Negros Oriental in the Philippines, known for its rural communities and agricultural economy.
-
B.
Taygi
Taygi is a lesser-known Samoyedic language of the Uralic family traditionally spoken by an indigenous group in northern Siberia.
-
C.
Takabisha
Takabisha is a record-breaking steel roller coaster in Japan renowned for its extremely steep drop and intense thrill elements.
-
D.
Tajuan
Tajuan is the given first name of former NFL cornerback Ty Law.
-
E.
Tahkuna
Tahkuna is a coastal settlement in northern Estonia, located on Hiiumaa Island and known for its proximity to the historic Tahkuna Lighthouse.
- 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_69d87f26864c819088365ca381a003c2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2da09dcf48190b6fdd14b1812c56a |
completed | April 18, 2026, 1:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00261cf0648190b4dd5ff79de7e315 |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:07 a.m.