Triple
T2986826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ankara Metro |
E80644
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object |
M3 line
The M3 line is a rapid transit route within the Ankara Metro system serving parts of Turkey’s capital city.
|
E320161
|
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: M3 line | Statement: [Ankara Metro, hasLine, M3 line]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: M3 line Context triple: [Ankara Metro, hasLine, M3 line]
-
A.
M2 line
The M2 line is a major rapid transit route within the Ankara Metro system in Turkey, serving key districts of the capital city.
-
B.
M1 line
The M1 line is a primary rapid transit route of the Ankara Metro system serving key districts of Turkey’s capital city.
-
C.
Metro Line 3
Metro Line 3 is a major Mexico City Metro route that runs north–south across the city, connecting key residential and commercial areas including the Gustavo A. Madero borough.
-
D.
Metro Line 53
Metro Line 53 is a rapid transit line in Amsterdam that connects the city center with the southeastern suburbs as part of the Amsterdam Metro system.
-
E.
Metro Line 4
Metro Line 4 is a rapid transit route within a city's metro system that connects with other lines, including Metro Line 6, to facilitate passenger transfers across the network.
- 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: M3 line Triple: [Ankara Metro, hasLine, M3 line]
Generated description
The M3 line is a rapid transit route within the Ankara Metro system serving parts of Turkey’s capital city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: M3 line Target entity description: The M3 line is a rapid transit route within the Ankara Metro system serving parts of Turkey’s capital city.
-
A.
M2 line
The M2 line is a major rapid transit route within the Ankara Metro system in Turkey, serving key districts of the capital city.
-
B.
M1 line
The M1 line is a primary rapid transit route of the Ankara Metro system serving key districts of Turkey’s capital city.
-
C.
Metro Line 3
Metro Line 3 is a major Mexico City Metro route that runs north–south across the city, connecting key residential and commercial areas including the Gustavo A. Madero borough.
-
D.
Metro Line 53
Metro Line 53 is a rapid transit line in Amsterdam that connects the city center with the southeastern suburbs as part of the Amsterdam Metro system.
-
E.
Metro Line 4
Metro Line 4 is a rapid transit route within a city's metro system that connects with other lines, including Metro Line 6, to facilitate passenger transfers across the network.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99c88f608190bf734e0b744bf3d1 |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1de999ff88190824ecdc164496d37 |
completed | March 11, 2026, 9:28 p.m. |
| NEDg | Description generation | batch_69b1df61bb208190b7714ff14ff81a99 |
completed | March 11, 2026, 9:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1dfd8fc5481908f1e130226d606fb |
completed | March 11, 2026, 9:34 p.m. |
Created at: March 8, 2026, 2:59 p.m.