Triple
T3402099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Songjiazhuang station |
E71678
|
entity |
| Predicate | servesLine |
P839
|
FINISHED |
| Object | Line 5 |
E67200
|
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: Line 5 | Statement: [Songjiazhuang station, servesLine, Line 5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 5 Context triple: [Songjiazhuang station, servesLine, Line 5]
-
A.
Line 5
Line 5 is a rapid transit line of the Shanghai Metro system serving the southern suburbs of the city.
-
B.
Line 5
Line 5 is one of the main lines of the Santiago Metro in Chile, running across several key districts and serving as a major east–west transit corridor in the city.
-
C.
Line 5
chosen
Line 5 is a major north–south route of the Beijing Subway known for connecting key residential and commercial areas through the city center.
-
D.
Line 5
Line 5 is one of the lines of the Mexico City Metro system, serving multiple stations across the city as part of its rapid transit network.
-
E.
Line 5
Line 5 is a major line of the Barcelona Metro rapid transit system, serving numerous key neighborhoods and transport hubs 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_69ad85aac4808190a092c9cc8911f584 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb8c96d7c8190a1f9d035996f79e3 |
completed | March 8, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bd4d02881909c365e054148ae8c |
completed | March 12, 2026, 11:27 p.m. |
Created at: March 8, 2026, 3:14 p.m.