Triple
T27474277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bǎojī |
E693410
|
entity |
| Predicate | countryOfEntityRomanized |
P193525
|
FINISHED |
| Object | 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: China | Statement: [Bǎojī, countryOfEntityRomanized, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfEntityRomanized Context triple: [Bǎojī, countryOfEntityRomanized, China]
-
A.
nameInLanguageRomanization
Indicates that an entity’s name is represented in the romanized (Latin-script) form of a particular language.
-
B.
commonNameCountry
Indicates that a given name is the commonly used or widely recognized name for a particular country.
-
C.
countrySpecificName
Indicates that an entity has a name or label that is specific to, or used within, a particular country.
-
D.
officialNameInRomaji
Indicates that an entity’s official name is written using the Roman alphabet (romaji) representation.
-
E.
institutionNativeName
Indicates that an institution is associated with its official or commonly used name in the institution’s original or local language.
- F. None of above. chosen
Provenance (4 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_69ef538105548190a771cc5a0cf8c211 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69fd49f6dbac81909744373a357b7982 |
completed | May 8, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69fd48ed68f481908374183c66a6b055 |
completed | May 8, 2026, 2:22 a.m. |
| PDg | Predicate description generation | batch_69fd49f612a4819096fe7d5a3bb439ba |
completed | May 8, 2026, 2:27 a.m. |
Created at: April 27, 2026, 12:56 p.m.