Triple
T14060869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jingshan |
E338340
|
entity |
| Predicate | positionRelativeToForbiddenCity |
P112666
|
FINISHED |
| Object | north of the Forbidden City |
—
|
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: north of the Forbidden City | Statement: [Jingshan, positionRelativeToForbiddenCity, north of the Forbidden City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionRelativeToForbiddenCity Context triple: [Jingshan, positionRelativeToForbiddenCity, north of the Forbidden City]
-
A.
positionInForbiddenCity
Indicates a spatial or locational relationship where an entity is situated at or within a specific place or position inside the Forbidden City.
-
B.
positionWithinForbiddenCity
Indicates that an entity’s location lies inside the spatial boundaries of the Forbidden City.
-
C.
distanceFromBeijingCityCenter
Indicates the physical distance between an entity’s location and the geographic center of Beijing city.
-
D.
positionInTemple
Indicates the specific location or role an entity occupies within a temple.
-
E.
positionOfStatue
Indicates the spatial location where a statue is situated or placed.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5686f51c81908c33143ecbaae83d |
completed | April 14, 2026, 3 p.m. |
| PD | Predicate disambiguation | batch_69de05adef888190b023ab42ef5076b6 |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de2398856c81908bed6070e4ca6ab1 |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 9, 2026, 10:21 p.m.