Triple
T4719965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Negros Island |
E104739
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Bago |
E276003
|
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: Bago | Statement: [Negros Island, hasCity, Bago]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bago Context triple: [Negros Island, hasCity, Bago]
-
A.
Bago
chosen
Bago is a component city in the province of Negros Occidental in the Philippines, known for its agricultural economy and historical significance.
-
B.
Bagana
Bagana is an active stratovolcano on Bougainville Island in Papua New Guinea, known for its frequent eruptions and extensive lava flows.
-
C.
Bara
Bara is a town in Pakistan’s Khyber District, known as a key settlement in the Khyber Pass region with strategic and commercial significance.
-
D.
Bologoye
Bologoye is a small town in western Russia known as a railway junction and transport hub between Moscow and St. Petersburg.
-
E.
Bages
Bages is a central comarca (county) in Catalonia, Spain, known for its historic town of Manresa and its mix of industrial, agricultural, and natural 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6428e9e081908ce4041183cad13b |
completed | March 20, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be108fe3b08190b3d306ca4b39860d |
completed | March 21, 2026, 3:29 a.m. |
Created at: March 20, 2026, 1:18 p.m.