Triple
T23789942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dingling Mausoleum |
E588063
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Soul Tower
Soul Tower is the prominent ceremonial structure that rises above the Dingling Mausoleum, marking the entrance to the underground tomb of the Ming dynasty emperor Wanli.
|
E1600677
|
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: Soul Tower | Statement: [Dingling Mausoleum, hasPart, Soul Tower]
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: Soul Tower Triple: [Dingling Mausoleum, hasPart, Soul Tower]
Generated description
Soul Tower is the prominent ceremonial structure that rises above the Dingling Mausoleum, marking the entrance to the underground tomb of the Ming dynasty emperor Wanli.
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_69e2490f4ad48190b690878eec3596c6 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1c6d73cf8819099fd8cbb9f93a908 |
completed | April 29, 2026, 8:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f53f151708190abf40df5ba0fb3d7 |
completed | May 21, 2026, 6:50 p.m. |
| NEDg | Description generation | batch_6a0f55b39aa081908fe70300b7639418 |
completed | May 21, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f5624dbc8819099593d440896bc54 |
completed | May 21, 2026, 6:59 p.m. |
Created at: April 17, 2026, 7:17 p.m.