Triple
T15228472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Trias |
E363936
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Tanza |
E363944
|
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: Tanza | Statement: [General Trias, borderedBy, Tanza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanza Context triple: [General Trias, borderedBy, Tanza]
-
A.
Tanza
chosen
Tanza is a coastal municipality in the province of Cavite in the Philippines, known for its historical significance and growing residential and industrial communities.
-
B.
Saña
Saña is a historic town in northern Peru known for its colonial heritage and association with early Spanish ecclesiastical figures.
-
C.
Natanz
Natanz is a town in central Iran’s Isfahan Province, known both for its historic architecture and for hosting one of the country’s key nuclear facilities.
-
D.
Bangala
Bangala is a regional variety of the Bantu language Lingala, spoken primarily in parts of the Democratic Republic of the Congo and neighboring areas.
-
E.
Tanca
Tanca was a historical figure known primarily as the assassin of King Jayanegara of the Majapahit Kingdom in 14th-century Java.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078ccdf48190b34eabd9e24e45a1 |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd39d42881908f2ad47613e23bfa |
completed | May 9, 2026, 7:07 a.m. |
Created at: April 10, 2026, 3:12 a.m.