Triple
T2515534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madonie |
E55403
|
entity |
| Predicate | hasPeak |
P8205
|
FINISHED |
| Object |
Monte Ferro
Monte Ferro is a mountain peak located within the Madonie mountain range in northern Sicily, Italy.
|
E274161
|
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 Ferro | Statement: [Madonie, hasPeak, Monte Ferro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Ferro Context triple: [Madonie, hasPeak, Monte Ferro]
-
A.
Monte Granero
Monte Granero is a prominent mountain peak in the western Italian Alps, known for its rugged terrain and scenic alpine landscapes.
-
B.
Cerro Jefe
Cerro Jefe is a prominent mountain in central Panama known for its cloud forests, biodiversity, and panoramic views over the surrounding isthmus.
-
C.
Cerro San Valentín
Cerro San Valentín is the highest mountain in Chilean Patagonia, rising prominently within the Northern Patagonian Ice Field of southern Chile.
-
D.
Cerro Otto
Cerro Otto is a scenic mountain in Argentina’s Patagonia region, popular for its panoramic views over Bariloche and its accessible hiking and cable car routes.
-
E.
Red Rocha
Red Rocha was an American professional basketball player and later coach who played as a center in the early years of the NBA, including for teams like the St. Louis Bombers and Syracuse Nationals.
- 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 Ferro Triple: [Madonie, hasPeak, Monte Ferro]
Generated description
Monte Ferro is a mountain peak located within the Madonie mountain range in northern Sicily, Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monte Ferro Target entity description: Monte Ferro is a mountain peak located within the Madonie mountain range in northern Sicily, Italy.
-
A.
Monte Granero
Monte Granero is a prominent mountain peak in the western Italian Alps, known for its rugged terrain and scenic alpine landscapes.
-
B.
Cerro Jefe
Cerro Jefe is a prominent mountain in central Panama known for its cloud forests, biodiversity, and panoramic views over the surrounding isthmus.
-
C.
Cerro San Valentín
Cerro San Valentín is the highest mountain in Chilean Patagonia, rising prominently within the Northern Patagonian Ice Field of southern Chile.
-
D.
Cerro Otto
Cerro Otto is a scenic mountain in Argentina’s Patagonia region, popular for its panoramic views over Bariloche and its accessible hiking and cable car routes.
-
E.
Red Rocha
Red Rocha was an American professional basketball player and later coach who played as a center in the early years of the NBA, including for teams like the St. Louis Bombers and Syracuse Nationals.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd20db7e0819096d901eb20ae65e5 |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2b975e6881909b70a1795e8e2776 |
completed | March 9, 2026, 8:20 p.m. |
| NEDg | Description generation | batch_69af461461d08190b50fa5ff80f1a774 |
completed | March 9, 2026, 10:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af467dd1c0819090bf8e01bbdb7e37 |
completed | March 9, 2026, 10:15 p.m. |
Created at: March 6, 2026, 9:46 p.m.