Triple
T28389405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 朝天门 |
E719108
|
entity |
| Predicate | 历史属性 |
P18777
|
FINISHED |
| Object | 重庆古城门之一 |
—
|
LITERAL 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: 重庆古城门之一 | Statement: [朝天门, 历史属性, 重庆古城门之一]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 历史属性 Context triple: [朝天门, 历史属性, 重庆古城门之一]
-
A.
historicalCharacteristic
chosen
Indicates that an entity possesses a trait, feature, or quality that is rooted in or defined by its history or past events.
-
B.
historicalType
Indicates that one entity classifies or characterizes another in terms of its role, status, or category within a historical context.
-
C.
historicalField
Indicates that one entity’s field of study, work, or relevance is situated within the historical domain or concerns past events.
-
D.
historical
Indicates that the subject has existed, occurred, or been relevant in the past rather than in the present or future.
-
E.
歴史
Indicates a relationship where something pertains to, records, or is involved in the events, development, or study of the past over time.
- F. None of above.
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_69eff6ef211081909d31d9be5f5567e6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64ceb933081909992f16507b0e667 |
completed | May 2, 2026, 7:13 p.m. |
| PD | Predicate disambiguation | batch_69f641e2f1708190b45b48d6a43c51d2 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 28, 2026, 1:12 a.m.