Triple
T14480060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air Base No. 5 Monte Real |
E359077
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Monte Real
Monte Real is a Portuguese town best known for hosting Air Base No. 5, an important installation of the Portuguese Air Force.
|
E1100663
|
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: Monte Real | Statement: [Air Base No. 5 Monte Real, locatedIn, Monte Real]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Real Context triple: [Air Base No. 5 Monte Real, locatedIn, Monte Real]
-
A.
Monte Castelo
Monte Castelo is a municipality in the state of Santa Catarina in southern Brazil, known for its rural landscape and small-town character.
-
B.
Monte Grande
Monte Grande is a suburban city in the Buenos Aires metropolitan area of Argentina, known as the administrative seat of the Esteban Echeverría Partido.
-
C.
Monte Francés
Monte Francés is a mountain that forms the highest peak on Isla Hoste in the remote southern region of Chilean Patagonia.
-
D.
Monte Renoso
Monte Renoso is a prominent mountain in southern Corsica, France, known for its rugged terrain and scenic alpine landscapes.
-
E.
Monte Rey
Monte Rey is an otter character known by the shortened name "Monte Rey."
- 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: Monte Real Triple: [Air Base No. 5 Monte Real, locatedIn, Monte Real]
Generated description
Monte Real is a Portuguese town best known for hosting Air Base No. 5, an important installation of the Portuguese Air Force.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monte Real Target entity description: Monte Real is a Portuguese town best known for hosting Air Base No. 5, an important installation of the Portuguese Air Force.
-
A.
Monte Castelo
Monte Castelo is a municipality in the state of Santa Catarina in southern Brazil, known for its rural landscape and small-town character.
-
B.
Monte Grande
Monte Grande is a suburban city in the Buenos Aires metropolitan area of Argentina, known as the administrative seat of the Esteban Echeverría Partido.
-
C.
Monte Francés
Monte Francés is a mountain that forms the highest peak on Isla Hoste in the remote southern region of Chilean Patagonia.
-
D.
Monte Renoso
Monte Renoso is a prominent mountain in southern Corsica, France, known for its rugged terrain and scenic alpine landscapes.
-
E.
Monte Rey
Monte Rey is an otter character known by the shortened name "Monte Rey."
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de924a576c819098351efabdb779b1 |
completed | April 14, 2026, 7:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd64a257488190818c65c1cc84c4b5 |
completed | May 8, 2026, 4:20 a.m. |
| NEDg | Description generation | batch_69fd6609ed5c8190a5d2c5fe25ea1467 |
completed | May 8, 2026, 4:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd666f81d08190a0d658b5949e0201 |
completed | May 8, 2026, 4:28 a.m. |
Created at: April 10, 2026, 1:20 a.m.