Triple
T3515139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuman |
E74287
|
entity |
| Predicate | hasLanguage |
P15
|
FINISHED |
| Object | Paipai |
E74842
|
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: Paipai | Statement: [Yuman, hasLanguage, Paipai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paipai Context triple: [Yuman, hasLanguage, Paipai]
-
A.
Paipai
chosen
Paipai are an indigenous people of northern Baja California, Mexico, traditionally speaking a Yuman language and maintaining distinct cultural and historical traditions in the region.
-
B.
Taobao
Taobao is a major Chinese online shopping platform known for its vast marketplace of consumer-to-consumer and business-to-consumer goods.
-
C.
PiTaPa
PiTaPa is a rechargeable contactless smart card system used for fare payment on public transportation networks in the Kansai region of Japan.
-
D.
Qibao
Qibao is an ancient water town and popular tourist area in Shanghai, known for its historic streets, canals, and traditional architecture.
-
E.
Guangjia
Guangjia was a warship of China’s late 19th-century Beiyang Fleet, one of the Qing dynasty’s principal modern naval forces.
- 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_69ad85cfb5c881909c9a2edd9d6043cc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc30362c81908ca7497a6a935cc6 |
completed | March 8, 2026, 6:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e7a5ab08190971c9dfd6550eb2d |
completed | March 13, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:19 p.m.