Triple
T7636022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanjing Lukou International Airport |
E172879
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Ma'anshan |
E95967
|
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: Ma'anshan | Statement: [Nanjing Lukou International Airport, nearbyCity, Ma'anshan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ma'anshan Context triple: [Nanjing Lukou International Airport, nearbyCity, Ma'anshan]
-
A.
Ma’anshan
chosen
Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
-
B.
Sihui
Sihui is a major Beijing Subway station in eastern Beijing that serves as a key interchange and endpoint for multiple metro lines.
-
C.
Baoshan
Baoshan is a prefecture-level city in southwestern China known for its mountainous landscapes, border trade, and location along historical routes connecting Yunnan to Myanmar.
-
D.
Lu'an
Lu'an is a prefecture-level city in western Anhui Province, China, known for its mountainous terrain and tea production.
-
E.
Panzhihua
Panzhihua is a major industrial city in southwestern China known for its rich mineral resources, especially vanadium-titanium magnetite, and its role as a key steel-producing center.
- 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_69c69952849881908fdcea7a93bfc307 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6faa95e488190962c23609e0890a5 |
completed | March 27, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c870c33ce081908df916d769fa84be |
completed | March 29, 2026, 12:22 a.m. |
Created at: March 27, 2026, 3:57 p.m.