Triple
T9337166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osraige |
E224673
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Mumu (Munster)
Mumu (Munster) is one of the traditional provinces of Ireland, located in the southwest and historically significant as a major Gaelic kingdom.
|
E793830
|
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: Mumu (Munster) | Statement: [Osraige, borderedBy, Mumu (Munster)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mumu (Munster) Context triple: [Osraige, borderedBy, Mumu (Munster)]
-
A.
Munster (Örtze)
Munster (Örtze) is a town in Lower Saxony, Germany, known for its large military training areas and proximity to the Lüneburg Heath.
-
B.
Merzen
Merzen is a rural municipality in Lower Saxony, Germany, known for its agricultural character and location within the Osnabrück region.
-
C.
Melle
Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
-
D.
Müggelheim
Müggelheim is a village-like district in the southeastern part of Berlin, Germany, characterized by its forests, lakes, and tranquil, semi-rural atmosphere.
-
E.
Mössinger
Mössinger is a German surname most notably borne by Ingrid Mössinger, a prominent figure in the German art and museum world.
- 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: Mumu (Munster) Triple: [Osraige, borderedBy, Mumu (Munster)]
Generated description
Mumu (Munster) is one of the traditional provinces of Ireland, located in the southwest and historically significant as a major Gaelic kingdom.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mumu (Munster) Target entity description: Mumu (Munster) is one of the traditional provinces of Ireland, located in the southwest and historically significant as a major Gaelic kingdom.
-
A.
Munster (Örtze)
Munster (Örtze) is a town in Lower Saxony, Germany, known for its large military training areas and proximity to the Lüneburg Heath.
-
B.
Merzen
Merzen is a rural municipality in Lower Saxony, Germany, known for its agricultural character and location within the Osnabrück region.
-
C.
Melle
Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
-
D.
Müggelheim
Müggelheim is a village-like district in the southeastern part of Berlin, Germany, characterized by its forests, lakes, and tranquil, semi-rural atmosphere.
-
E.
Mössinger
Mössinger is a German surname most notably borne by Ingrid Mössinger, a prominent figure in the German art and museum world.
- 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_69ca84286fcc81909f6e7fd7a7e862a2 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd37f161e481908e23c1ec7e5fcf97 |
completed | April 1, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0e3d8316081908cb9ea36eb069c2d |
completed | April 4, 2026, 10:11 a.m. |
| NEDg | Description generation | batch_69d0e57272cc819085a1fd3e356d7c46 |
completed | April 4, 2026, 10:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0e72c3d088190953a5929f8b861d8 |
completed | April 4, 2026, 10:25 a.m. |
Created at: March 30, 2026, 7:40 p.m.