Triple
T7103018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexander of Greece |
E165505
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Glücksburg
Glücksburg is a European royal house of German origin that has provided monarchs to several countries, including Denmark, Norway, and Greece.
|
E645792
|
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: Glücksburg | Statement: [Alexander of Greece, familyName, Glücksburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glücksburg Context triple: [Alexander of Greece, familyName, Glücksburg]
-
A.
Lauenburg
Lauenburg is a historic town in northern Germany situated on the banks of the Elbe River.
-
B.
Heringsdorf
Heringsdorf is a seaside resort town on the Baltic Sea coast of the island of Usedom in northeastern Germany, known for its historic pier and spa architecture.
-
C.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
D.
Teterow
Teterow is a small historic town in northeastern Germany known for its medieval architecture and location in the Mecklenburg Lake District.
-
E.
Schöppingen
Schöppingen is a small municipality in North Rhine-Westphalia, Germany, known for its rural character and location near the Dutch border.
- 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: Glücksburg Triple: [Alexander of Greece, familyName, Glücksburg]
Generated description
Glücksburg is a European royal house of German origin that has provided monarchs to several countries, including Denmark, Norway, and Greece.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Glücksburg Target entity description: Glücksburg is a European royal house of German origin that has provided monarchs to several countries, including Denmark, Norway, and Greece.
-
A.
Lauenburg
Lauenburg is a historic town in northern Germany situated on the banks of the Elbe River.
-
B.
Heringsdorf
Heringsdorf is a seaside resort town on the Baltic Sea coast of the island of Usedom in northeastern Germany, known for its historic pier and spa architecture.
-
C.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
D.
Teterow
Teterow is a small historic town in northeastern Germany known for its medieval architecture and location in the Mecklenburg Lake District.
-
E.
Schöppingen
Schöppingen is a small municipality in North Rhine-Westphalia, Germany, known for its rural character and location near the Dutch border.
- 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_69c6887fcddc8190a5d58908f6dee590 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e58a0a2c819088e0c8874fb4491f |
completed | March 27, 2026, 8:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7ad83aa288190b06262ac9898f5c1 |
completed | March 28, 2026, 10:29 a.m. |
| NEDg | Description generation | batch_69c7ae661f4481908ee489023af9603b |
completed | March 28, 2026, 10:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7aef767848190b7edf7a99e2e019d |
completed | March 28, 2026, 10:35 a.m. |
Created at: March 27, 2026, 2:42 p.m.