Triple
T23219403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inharrime District |
E580844
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Panda District |
—
|
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: Panda District | Statement: [Inharrime District, borders, Panda District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panda District Context triple: [Inharrime District, borders, Panda District]
-
A.
Panda District
chosen
Panda District is an administrative district located within Inhambane Province in southern Mozambique.
-
B.
Mapo District
Mapo District is a vibrant administrative and cultural area in western Seoul, South Korea, known for neighborhoods like Hongdae and its lively arts, nightlife, and dining scenes.
-
C.
Yanta District
Yanta District is an urban district of Xi'an in Shaanxi Province, China, known for its cultural and historical landmarks and educational institutions.
-
D.
Nanpiao District
Nanpiao District is an administrative district under the jurisdiction of Huludao City in Liaoning Province, northeastern China.
-
E.
Fang District
Fang District is a northern Thai district known for its mountainous landscapes, agricultural production, and proximity to the Myanmar border.
- 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_69e2460389408190be74f41d217799a9 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f191675de48190858907872a065c56 |
completed | April 29, 2026, 5:04 a.m. |
Created at: April 17, 2026, 4:08 p.m.