Triple
T24853596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lúzhōu |
E621957
|
entity |
| Predicate | romanizesToponym |
P157446
|
FINISHED |
| Object | Luzhou, Sichuan, China |
—
|
NE NERFINISHED |
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: Luzhou, Sichuan, China | Statement: [Lúzhōu, romanizesToponym, Luzhou, Sichuan, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanizesToponym Context triple: [Lúzhōu, romanizesToponym, Luzhou, Sichuan, China]
-
A.
romanizationOfToponymType
Indicates a relationship where a specific type of place-name is expressed in a romanized (Latin-script) form corresponding to its original writing system.
-
B.
romanizationFrom
Indicates that one entity is a romanized representation derived from the script or writing system of another entity.
-
C.
romanizationProcess
Indicates the process of converting text from a non-Latin writing system into a representation using the Latin alphabet.
-
D.
exampleRomanization
chosen
Indicates that one entity is a romanized representation (in Latin script) of the other entity’s original text or name.
-
E.
romanizationVariantOf
Indicates that one written form is a different romanized representation of the same underlying word or expression as another.
- F. None of above.
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_69e2fac297e481909d3aedc75f585e42 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d6ef33081908f5d36ba1ae5f473 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 5:21 a.m.