Triple
T20967610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wijk bij Duurstede |
E516409
|
entity |
| Predicate | historicalName |
P65
|
FINISHED |
| Object |
Dorestad
Dorestad was a major early medieval Frisian-Frankish trading center and port on the Rhine, which flourished between the 7th and 9th centuries in what is now the Netherlands.
|
E1459334
|
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: Dorestad | Statement: [Wijk bij Duurstede, historicalName, Dorestad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dorestad Context triple: [Wijk bij Duurstede, historicalName, Dorestad]
-
A.
Dieburg
Dieburg is a small historic town in the German state of Hesse, known for its medieval old town and regional administrative role.
-
B.
Stitswerd
Stitswerd is a small village in the northern Netherlands, located in the province of Groningen.
-
C.
Deventer
Deventer is a historic Dutch city known for its medieval architecture, Hanseatic trading past, and annual book market.
-
D.
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.
-
E.
Doesburg
Doesburg is a historic city in the Dutch province of Gelderland, known for its well-preserved medieval center and location at the confluence of the IJssel and Oude IJssel rivers.
- 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: Dorestad Triple: [Wijk bij Duurstede, historicalName, Dorestad]
Generated description
Dorestad was a major early medieval Frisian-Frankish trading center and port on the Rhine, which flourished between the 7th and 9th centuries in what is now the Netherlands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dorestad Target entity description: Dorestad was a major early medieval Frisian-Frankish trading center and port on the Rhine, which flourished between the 7th and 9th centuries in what is now the Netherlands.
-
A.
Dieburg
Dieburg is a small historic town in the German state of Hesse, known for its medieval old town and regional administrative role.
-
B.
Stitswerd
Stitswerd is a small village in the northern Netherlands, located in the province of Groningen.
-
C.
Deventer
Deventer is a historic Dutch city known for its medieval architecture, Hanseatic trading past, and annual book market.
-
D.
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.
-
E.
Doesburg
Doesburg is a historic city in the Dutch province of Gelderland, known for its well-preserved medieval center and location at the confluence of the IJssel and Oude IJssel rivers.
- 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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb9d6b548190af7214ad2468cfbf |
completed | April 21, 2026, 4:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09279ce78c8190be70cc957da851e6 |
completed | May 17, 2026, 2:27 a.m. |
| NEDg | Description generation | batch_6a0928e11bc481909b4350770db28671 |
completed | May 17, 2026, 2:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0929e0fb308190a224fe9dceb74a1c |
completed | May 17, 2026, 2:37 a.m. |
Created at: April 16, 2026, 1:37 p.m.