Triple
T33063055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Town, Penang |
E846022
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Cheong Fatt Tze Mansion
Cheong Fatt Tze Mansion is a historic indigo-blue Chinese courtyard house in George Town, Penang, renowned for its eclectic architectural style and heritage significance.
|
E2034439
|
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: Cheong Fatt Tze Mansion | Statement: [George Town, Penang, hasLandmark, Cheong Fatt Tze Mansion]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Cheong Fatt Tze Mansion Triple: [George Town, Penang, hasLandmark, Cheong Fatt Tze Mansion]
Generated description
Cheong Fatt Tze Mansion is a historic indigo-blue Chinese courtyard house in George Town, Penang, renowned for its eclectic architectural style and heritage significance.
Provenance (5 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_69f3495333b8819095e9af56855b9061 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d37b7d14819084cb07649223db35 |
completed | May 3, 2026, 4:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a34e525cc3881909e6c57f1ef5b4d14 |
completed | June 19, 2026, 6:43 a.m. |
| NEDg | Description generation | batch_6a34e5cf97c08190a6221df36b9d99fa |
completed | June 19, 2026, 6:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a34e6d01ce08190b4edfcda322afae0 |
completed | June 19, 2026, 6:50 a.m. |
Created at: May 1, 2026, 1:25 a.m.