Triple
T16463235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helaba |
E399863
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Helaba |
E399863
|
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: Helaba | Statement: [Helaba, shortName, Helaba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helaba Context triple: [Helaba, shortName, Helaba]
-
A.
Helaba
chosen
Helaba is a major German public-sector commercial bank, formally known as Landesbank Hessen-Thüringen, that provides wholesale, retail, and public finance services.
-
B.
Murias
Murias are an indigenous tribal community of the Bastar region in central India, known for their distinct cultural traditions, social organization, and art forms.
-
C.
La Verde
La Verde is the popular nickname of the Bolivia national football team, referencing the green color of their home kit.
-
D.
Guática
Guática is a small municipality and town in the Colombian department of Risaralda, known for its rural Andean landscapes and coffee-growing economy.
-
E.
Teba
Teba is a town in the province of Málaga, Spain, historically notable as the site of a major medieval battle during the Reconquista.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32d824cd881909b1f2fd40e14ee35 |
completed | April 18, 2026, 7:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f555f6081908b1f0d524b6fb9a7 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:10 a.m.