Triple
T14468901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter of Pisa |
E358784
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Aachen |
E43082
|
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: Aachen | Statement: [Peter of Pisa, residence, Aachen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aachen Context triple: [Peter of Pisa, residence, Aachen]
-
A.
Aachen
chosen
Aachen is a historic German city near the borders with Belgium and the Netherlands, renowned for its medieval cathedral, role as a coronation site for Holy Roman Emperors, and significance in both World Wars.
-
B.
Trier
Trier is a historic city in western Germany, renowned as one of the country’s oldest cities with extensive Roman ruins and medieval landmarks.
-
C.
Neuss
Neuss is a city in western Germany, near Düsseldorf, known as an administrative and commercial center with historical roots dating back to Roman times.
-
D.
Maubeuge
Maubeuge is a fortified industrial town in northern France near the Belgian border, historically significant for its strategic military position.
-
E.
Neunkirchen
Neunkirchen is an industrial town in Austria’s Lower Austria region, known historically for its manufacturing and metalworking industries.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91f8613c819080424104c0b7f4c3 |
completed | April 14, 2026, 7:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf070e23481908528274ce5e10731 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:20 a.m.