Triple
T17026272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tan (談) |
E413070
|
entity |
| Predicate | otherRomanization |
P2508
|
FINISHED |
| Object | T’an (Wade–Giles) |
—
|
LITERAL 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: T’an (Wade–Giles) | Statement: [Tan (談), otherRomanization, T’an (Wade–Giles)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherRomanization Context triple: [Tan (談), otherRomanization, T’an (Wade–Giles)]
-
A.
hasRomanizationOf
chosen
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
B.
romanizationFrom
Indicates that one entity is a romanized representation derived from the script or writing system of another entity.
-
C.
hasRomanizationStandard
Indicates that an entity’s romanized form follows a specified romanization standard or system.
-
D.
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.
-
E.
romanizationProcess
Indicates the process of converting text from a non-Latin writing system into a representation using the Latin alphabet.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
| PD | Predicate disambiguation | batch_69e35d5be7f48190af9db67a1e23850f |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.