Triple
T29488118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zengcheng District |
E747987
|
entity |
| Predicate | positionRelativeToGuangzhou |
P195081
|
FINISHED |
| Object | eastern |
—
|
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: eastern | Statement: [Zengcheng District, positionRelativeToGuangzhou, eastern]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionRelativeToGuangzhou Context triple: [Zengcheng District, positionRelativeToGuangzhou, eastern]
-
A.
positionOnChina
Indicates the stance, opinion, or policy that one entity holds regarding China.
-
B.
ChinaPosition
Indicates the stance, policy, or viewpoint officially held or expressed by China regarding a particular issue, event, or entity.
-
C.
positionRelativeToForbiddenCity
Indicates the spatial relationship of an entity’s location with respect to the Forbidden City.
-
D.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
-
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_69f0bd43ba30819095eb1cfc3adf525c |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69fda5003cdc8190a558501271389912 |
completed | May 8, 2026, 8:55 a.m. |
| PD | Predicate disambiguation | batch_69fda05bfc2c819096821a5300e9bb24 |
completed | May 8, 2026, 8:35 a.m. |
| PDg | Predicate description generation | batch_69fda4fe67a08190bdaa84ceabd6de6c |
completed | May 8, 2026, 8:55 a.m. |
Created at: April 28, 2026, 4:10 p.m.