Triple
T15030202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green line (Stockholm metro) |
E378322
|
entity |
| Predicate | hasLineCode |
P19896
|
FINISHED |
| Object | Green line |
E378322
|
NE FINISHED |
How this triple was built (2 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: Green line | Statement: [Green line (Stockholm metro), hasLineCode, Green line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green line Context triple: [Green line (Stockholm metro), hasLineCode, Green line]
-
A.
Green line
chosen
The Green line is one of the main color-coded routes in the Stockholm metro system, serving numerous central and suburban stations across the city.
-
B.
Green line
The Green line is a major rapid transit route on the Barcelona Metro system, serving numerous central and outlying neighborhoods across the city.
-
C.
green line
The green line refers to the Zamoskvoretskaya Line, one of the busiest and oldest lines of the Moscow Metro system.
-
D.
Blue line
The Blue line is one of the main lines of the Stockholm metro system, connecting central Stockholm with several northern and western suburbs.
-
E.
Red line
The Red line is one of the main color-coded routes of the Stockholm metro system, serving numerous central and suburban stations across the city.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69feb7db6f0081909ab35435c1e4ad13 |
completed | May 9, 2026, 4:28 a.m. |
Created at: April 10, 2026, 2:59 a.m.