Triple
T1338891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Da Nang |
E28418
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Hue |
E116514
|
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: Hue | Statement: [Da Nang, near, Hue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hue Context triple: [Da Nang, near, Hue]
-
A.
Hue
chosen
Hue is a historic city in central Vietnam that served as the former imperial capital and was a major battleground during the Vietnam War.
-
B.
Goodhue
Goodhue is a surname most notably associated with Bertram Grosvenor Goodhue, an influential American architect known for his Gothic Revival and early modernist designs.
-
C.
Ochre City
Ochre City is a popular nickname for Marrakesh, referring to the Moroccan city's distinctive red and ochre-colored buildings and walls.
-
D.
Kota
Kota is a major industrial and educational city in southeastern Rajasthan, India, known for its coaching institutes and power plants along the Chambal River.
-
E.
Lichtstad
Lichtstad is the Dutch nickname for the city of Eindhoven, reflecting its historic association with the lighting industry and companies like Philips.
- 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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c21303c881908fef0b32831222fe |
completed | March 1, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acc62e4c788190824df2a9b81692d7 |
completed | March 8, 2026, 12:43 a.m. |
Created at: March 1, 2026, 7:56 p.m.