Triple
T18054874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eordaia |
E432010
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Mouriki
Mouriki is a settlement located within the municipality of Eordaia in the Western Macedonia region of Greece.
|
E1302963
|
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: Mouriki | Statement: [Eordaia, containsSettlement, Mouriki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mouriki Context triple: [Eordaia, containsSettlement, Mouriki]
-
A.
Mogareeka
Mogareeka is a small coastal locality in New South Wales, Australia, known for its beaches and estuarine scenery near Tathra.
-
B.
Mookajji
Mookajji is the wise, introspective grandmother figure at the heart of the Kannada novel "Mookajjiya Kanasugalu," known for her visionary dreams and philosophical reflections on life and tradition.
-
C.
Morvi
Morvi is a town in the Morbi district of Gujarat, India, historically known as the seat of the former princely Morvi State.
-
D.
Mugatu
Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
-
E.
Mandras
Mandras is a passionate but troubled young fisherman and resistance fighter in Louis de Bernières’ novel "Captain Corelli’s Mandolin," whose experiences in World War II strain his love for Pelagia and transform his character.
- 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: Mouriki Triple: [Eordaia, containsSettlement, Mouriki]
Generated description
Mouriki is a settlement located within the municipality of Eordaia in the Western Macedonia region of Greece.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mouriki Target entity description: Mouriki is a settlement located within the municipality of Eordaia in the Western Macedonia region of Greece.
-
A.
Mogareeka
Mogareeka is a small coastal locality in New South Wales, Australia, known for its beaches and estuarine scenery near Tathra.
-
B.
Mookajji
Mookajji is the wise, introspective grandmother figure at the heart of the Kannada novel "Mookajjiya Kanasugalu," known for her visionary dreams and philosophical reflections on life and tradition.
-
C.
Morvi
Morvi is a town in the Morbi district of Gujarat, India, historically known as the seat of the former princely Morvi State.
-
D.
Mugatu
Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
-
E.
Mandras
Mandras is a passionate but troubled young fisherman and resistance fighter in Louis de Bernières’ novel "Captain Corelli’s Mandolin," whose experiences in World War II strain his love for Pelagia and transform his character.
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4c101dfb081908dc66f4b4967d8c7 |
completed | April 19, 2026, 11:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0349c130788190b6c4aeaf2e1f2e97 |
completed | May 12, 2026, 3:39 p.m. |
| NEDg | Description generation | batch_6a034b81d3a4819087f9ead00f13635e |
completed | May 12, 2026, 3:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a034c190a58819096c77a2254511256 |
completed | May 12, 2026, 3:49 p.m. |
Created at: April 10, 2026, 10:26 a.m.