Triple
T6577983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 11 (Madrid Metro) |
E157216
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Opañel
Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
|
E599414
|
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: Opañel | Statement: [Line 11 (Madrid Metro), hasStation, Opañel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Opañel Context triple: [Line 11 (Madrid Metro), hasStation, Opañel]
-
A.
Ōpunake
Ōpunake is a small coastal town on the west coast of New Zealand’s North Island, known for its surf beach and views of Mount Taranaki.
-
B.
Papingo
Papingo is a picturesque traditional village in the Zagori region of Epirus, northwestern Greece, known for its stone architecture and dramatic mountain scenery.
-
C.
Nagapasha
Nagapasha is a mythical serpent-noose weapon from Hindu epics, famed for binding its targets with powerful, inescapable snake bonds.
-
D.
Opon
Opon is the former name of what is now Lapu-Lapu City, a highly urbanized city located on Mactan Island in the Philippines.
-
E.
Opebi
Opebi is a commercial and residential neighborhood in Lagos, Nigeria, known for its busy Opebi Road, offices, shops, and proximity to major hubs in Ikeja.
- 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: Opañel Triple: [Line 11 (Madrid Metro), hasStation, Opañel]
Generated description
Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Opañel Target entity description: Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
-
A.
Ōpunake
Ōpunake is a small coastal town on the west coast of New Zealand’s North Island, known for its surf beach and views of Mount Taranaki.
-
B.
Papingo
Papingo is a picturesque traditional village in the Zagori region of Epirus, northwestern Greece, known for its stone architecture and dramatic mountain scenery.
-
C.
Nagapasha
Nagapasha is a mythical serpent-noose weapon from Hindu epics, famed for binding its targets with powerful, inescapable snake bonds.
-
D.
Opon
Opon is the former name of what is now Lapu-Lapu City, a highly urbanized city located on Mactan Island in the Philippines.
-
E.
Opebi
Opebi is a commercial and residential neighborhood in Lagos, Nigeria, known for its busy Opebi Road, offices, shops, and proximity to major hubs in Ikeja.
- 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_69c6882b3a108190b3a9eb343ae4162c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae74fd90819091d67eec6381d5e0 |
completed | March 27, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cba5cc708190a8748160a7878b8f |
completed | March 27, 2026, 6:25 p.m. |
| NEDg | Description generation | batch_69c6cd08a9c88190a481d4d3f8e680bf |
completed | March 27, 2026, 6:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6cdc859cc8190bbae2efc39409021 |
completed | March 27, 2026, 6:34 p.m. |
Created at: March 27, 2026, 1:54 p.m.