Triple
T12887120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darmstadt-Dieburg |
E308256
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Dieburg
Dieburg is a small historic town in the German state of Hesse, known for its medieval old town and regional administrative role.
|
E1166156
|
NE FINISHED |
How this triple was built (4 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: Dieburg | Statement: [Darmstadt-Dieburg, contains, Dieburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dieburg Context triple: [Darmstadt-Dieburg, contains, Dieburg]
-
A.
Venlo
Venlo is a historic city in the southeastern Netherlands, located near the German border on the river Meuse and known as a regional economic and logistics hub.
-
B.
Roermond
Roermond is a historic city in the southeastern Netherlands known for its medieval architecture, prominent churches, and large designer outlet shopping center.
-
C.
Culemborg
Culemborg is a historic town in the Dutch province of Gelderland, known for its medieval center and role in the early Dutch colonial era.
-
D.
Deventer
Deventer is a historic Dutch city known for its medieval architecture, Hanseatic trading past, and annual book market.
-
E.
Turnhout
Turnhout is a historic city in northern Belgium known for its playing card industry, cultural heritage, and role as a regional center in the Kempen area.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Dieburg Triple: [Darmstadt-Dieburg, contains, Dieburg]
Generated description
Dieburg is a small historic town in the German state of Hesse, known for its medieval old town and regional administrative role.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dieburg Target entity description: Dieburg is a small historic town in the German state of Hesse, known for its medieval old town and regional administrative role.
-
A.
Venlo
Venlo is a historic city in the southeastern Netherlands, located near the German border on the river Meuse and known as a regional economic and logistics hub.
-
B.
Roermond
Roermond is a historic city in the southeastern Netherlands known for its medieval architecture, prominent churches, and large designer outlet shopping center.
-
C.
Culemborg
Culemborg is a historic town in the Dutch province of Gelderland, known for its medieval center and role in the early Dutch colonial era.
-
D.
Deventer
Deventer is a historic Dutch city known for its medieval architecture, Hanseatic trading past, and annual book market.
-
E.
Turnhout
Turnhout is a historic city in northern Belgium known for its playing card industry, cultural heritage, and role as a regional center in the Kempen area.
- F. None of above. chosen
Provenance (5 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714415c08190aa9944b494a3ddad |
completed | April 10, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff56ade75c8190b556c3b0ba692a96 |
completed | May 9, 2026, 3:45 p.m. |
| NEDg | Description generation | batch_69ff5858f6f88190a94a871c831e4f78 |
completed | May 9, 2026, 3:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff589de85c8190abc9c888ac90cf52 |
completed | May 9, 2026, 3:54 p.m. |
Created at: April 9, 2026, 5:39 p.m.