Triple
T14554282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Military Museum station |
E341497
|
entity |
| Predicate | hasLine |
P35
|
FINISHED |
| Object | Line 9 |
E68471
|
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 9 | Statement: [Military Museum station, hasLine, Line 9]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 9 Context triple: [Military Museum station, hasLine, Line 9]
-
A.
Line 9
chosen
Line 9 is a rapid transit line of the Beijing Subway system that serves as part of the city's urban rail network.
-
B.
Line 9
Line 9 is a line of the Mexico City Metro system that serves as one of its key rapid transit routes across the city.
-
C.
Line 9
Line 9 is a major Barcelona Metro line designed as a long, partially automated route connecting key suburban and airport areas with the wider metropolitan network.
-
D.
Line 9
Line 9 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving several key districts in the city.
-
E.
Line 9
Line 9 is a rapid transit route within the STC Metro system, serving as one of its numbered urban rail lines.
- 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_69d822db9c8481908213ceb39585f792 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb2f00cec8190a7b6482d18b9a216 |
completed | April 14, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ab9a5ac81908779a3c8701353fa |
completed | May 8, 2026, 7:03 a.m. |
Created at: April 10, 2026, 1:23 a.m.