Triple
T3928674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Main Tower |
E93339
|
entity |
| Predicate | occupant |
P75
|
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: [Main Tower, occupant, Helaba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Helaba Context triple: [Main Tower, occupant, 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.
Labayu
Labayu was a 14th-century BCE Canaanite ruler known from the Amarna letters for his aggressive expansionism and conflicts with neighboring city-states.
-
C.
Ilo
Ilo is a given name associated with the individual Ilo Browne Wallace.
-
D.
Moura
Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
-
E.
Cieneguilla
Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeda65b708190b24cd715915aec1d |
completed | March 9, 2026, 3:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b53387775881909479f4e1fcecdaca |
completed | March 14, 2026, 10:08 a.m. |
Created at: March 9, 2026, 3:23 p.m.