Triple
T15567640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mealhada |
E374156
|
entity |
| Predicate | hasAdministrativeDivision |
P747
|
FINISHED |
| Object |
Luso
Luso is a Portuguese civil parish in the municipality of Mealhada, known for its mineral water springs and proximity to the Bussaco Forest.
|
E1165501
|
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: Luso | Statement: [Mealhada, hasAdministrativeDivision, Luso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luso Context triple: [Mealhada, hasAdministrativeDivision, Luso]
-
A.
Luso
Luso is the former colonial-era name of the city now known as Luena, the capital of Moxico Province in eastern Angola.
-
B.
Lusones
The Lusones were an ancient Celtiberian people who inhabited part of the central Iberian Peninsula during the pre-Roman and Roman Republican periods.
-
C.
Portogruaro
Portogruaro is a historic town in northeastern Italy’s Veneto region, known for its medieval architecture and canals.
-
D.
Portuguesa
Portuguesa is a state in western Venezuela known for its extensive agricultural production, particularly of rice and corn, earning it the nickname "the Granary of Venezuela."
-
E.
Azambuja
Azambuja is a municipality in Portugal known for its agricultural landscape and proximity to the Lisbon metropolitan 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: Luso Triple: [Mealhada, hasAdministrativeDivision, Luso]
Generated description
Luso is a Portuguese civil parish in the municipality of Mealhada, known for its mineral water springs and proximity to the Bussaco Forest.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Luso Target entity description: Luso is a Portuguese civil parish in the municipality of Mealhada, known for its mineral water springs and proximity to the Bussaco Forest.
-
A.
Luso
Luso is the former colonial-era name of the city now known as Luena, the capital of Moxico Province in eastern Angola.
-
B.
Lusones
The Lusones were an ancient Celtiberian people who inhabited part of the central Iberian Peninsula during the pre-Roman and Roman Republican periods.
-
C.
Portogruaro
Portogruaro is a historic town in northeastern Italy’s Veneto region, known for its medieval architecture and canals.
-
D.
Portuguesa
Portuguesa is a state in western Venezuela known for its extensive agricultural production, particularly of rice and corn, earning it the nickname "the Granary of Venezuela."
-
E.
Azambuja
Azambuja is a municipality in Portugal known for its agricultural landscape and proximity to the Lisbon metropolitan 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04dde90b081908284d9258d4462e3 |
completed | April 16, 2026, 2:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4219a081909acca9f783ecd44b |
completed | May 9, 2026, 3:01 p.m. |
| NEDg | Description generation | batch_69ff50d54960819089491ccb580784b8 |
completed | May 9, 2026, 3:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff5208e9a08190b4a6f4157cf3c237 |
completed | May 9, 2026, 3:26 p.m. |
Created at: April 10, 2026, 4:10 a.m.