Triple
T20037952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taichung |
E497326
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object | Dallas |
—
|
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: Dallas | Statement: [Taichung, hasSisterCity, Dallas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dallas Context triple: [Taichung, hasSisterCity, Dallas]
-
A.
Dallas
Dallas is a small city in Paulding County, Georgia, known for its historic downtown and location within the state's mineral-rich Georgia Gold Belt region.
-
B.
Dallas
Dallas is a central female character in the classic Western film "Stagecoach," portrayed as a compassionate yet socially ostracized woman whose journey reveals themes of dignity and redemption.
-
C.
Dallas
Dallas is the calm, pragmatic captain of the commercial starship Nostromo in the 1979 science fiction horror film "Alien."
-
D.
Dallas
Dallas is a popular American comic strip created by cartoonist Jim Davis.
-
E.
Dallas
chosen
Dallas is a major city in north Texas known for its role as a commercial and cultural hub, with a rich history in railroads, oil, and telecommunications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e935ac8190900cdb4f0cfde505 |
completed | April 20, 2026, 5:31 p.m. |
Created at: April 11, 2026, 3:36 p.m.