Triple
T8258592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai South Railway Station |
E193132
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object | SHH |
E193132
|
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: SHH | Statement: [Shanghai South Railway Station, hasStationCode, SHH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SHH Context triple: [Shanghai South Railway Station, hasStationCode, SHH]
-
A.
SHH
chosen
SHH is the railway station code used to identify Shanghai South Railway Station in China’s rail network.
-
B.
HSH
HSH is the formal abbreviation used to address or refer to a person holding the noble style "His Serene Highness."
-
C.
SH
SH is the common abbreviation for Södertörn University, a higher education institution located in the Stockholm region of Sweden.
-
D.
SH
SH is the vehicle registration code for the Swiss canton of Schaffhausen.
-
E.
Shush
Shush is a modern Iranian city that encompasses the archaeological remains of ancient Susa, one of the oldest and most important cities of the ancient Near East.
- 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_69ca82dfad9c8190b8cd18fb89f50f40 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78fe5e2c819080741ea24bae0807 |
completed | March 31, 2026, 7:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd355bef508190894bd01ec39e83f6 |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 5:49 p.m.