Triple
T35906077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhonghai Lake |
E1038474
|
entity |
| Predicate | relativePositionInZhongnanhai |
P206102
|
FINISHED |
| Object | central lake |
—
|
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: central lake | Statement: [Zhonghai Lake, relativePositionInZhongnanhai, central lake]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativePositionInZhongnanhai Context triple: [Zhonghai Lake, relativePositionInZhongnanhai, central lake]
-
A.
positionRelativeToGuangzhou
Indicates the spatial or geographic position of an entity in relation to the city of Guangzhou.
-
B.
positionOnChina
Indicates the stance, opinion, or policy that one entity holds regarding China.
-
C.
positionRelativeToForbiddenCity
Indicates the spatial relationship of an entity’s location with respect to the Forbidden City.
-
D.
ChinaPosition
Indicates the stance, policy, or viewpoint officially held or expressed by China regarding a particular issue, event, or entity.
-
E.
distanceFromBeijingCityCenter
Indicates the physical distance between an entity’s location and the geographic center of Beijing city.
- F. None of above. chosen
Provenance (4 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_69f76e2259608190bf6788a132e0d139 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037ce53de881908cf14141cf3bc570 |
completed | May 12, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:07 p.m.