Triple
T15030210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green line (Stockholm metro) |
E378322
|
entity |
| Predicate | hasTerminus |
P388
|
FINISHED |
| Object |
Åkeshov
Åkeshov is a Stockholm metro station in the western suburbs, serving as a terminus on one branch of the system’s Green line.
|
E1134021
|
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: Åkeshov | Statement: [Green line (Stockholm metro), hasTerminus, Åkeshov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Åkeshov Context triple: [Green line (Stockholm metro), hasTerminus, Åkeshov]
-
A.
Kovel
Kovel is a historic town in northwestern Ukraine, located in the Volyn region and known as a former important railway and trade hub.
-
B.
Boshof
Boshof is a small town in South Africa’s Free State province, historically known for its agricultural economy and role in the Anglo-Boer War.
-
C.
Lvovna
Lvovna is a Russian patronymic derived from the male given name Lev, traditionally used as the middle name for daughters of men named Lev.
-
D.
Kozelets
Kozelets is an urban-type settlement in northern Ukraine, historically known as a local administrative and trading center.
-
E.
Oziersk
Oziersk is a town in Russia’s Kaliningrad Oblast, historically linked to nearby Polish regions and engaged in cross-border cooperation with places like Gołdap.
- 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: Åkeshov Triple: [Green line (Stockholm metro), hasTerminus, Åkeshov]
Generated description
Åkeshov is a Stockholm metro station in the western suburbs, serving as a terminus on one branch of the system’s Green line.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Åkeshov Target entity description: Åkeshov is a Stockholm metro station in the western suburbs, serving as a terminus on one branch of the system’s Green line.
-
A.
Kovel
Kovel is a historic town in northwestern Ukraine, located in the Volyn region and known as a former important railway and trade hub.
-
B.
Boshof
Boshof is a small town in South Africa’s Free State province, historically known for its agricultural economy and role in the Anglo-Boer War.
-
C.
Lvovna
Lvovna is a Russian patronymic derived from the male given name Lev, traditionally used as the middle name for daughters of men named Lev.
-
D.
Kozelets
Kozelets is an urban-type settlement in northern Ukraine, historically known as a local administrative and trading center.
-
E.
Oziersk
Oziersk is a town in Russia’s Kaliningrad Oblast, historically linked to nearby Polish regions and engaged in cross-border cooperation with places like Gołdap.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e2416081908dfba48d7f7b4a84 |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dd967588190821cf47e9734db21 |
completed | May 9, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69fe9e5dbbe0819084567688758b0245 |
completed | May 9, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe9eedca1481908ce438991184d62e |
completed | May 9, 2026, 2:41 a.m. |
Created at: April 10, 2026, 2:59 a.m.