Triple
T3526970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seif Palace |
E74561
|
entity |
| Predicate | nearbyFunction |
P28961
|
FINISHED |
| Object | adjacent to other central government institutions |
—
|
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: adjacent to other central government institutions | Statement: [Seif Palace, nearbyFunction, adjacent to other central government institutions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyFunction Context triple: [Seif Palace, nearbyFunction, adjacent to other central government institutions]
-
A.
nearbyCurrent
Indicates that one entity is located close to another entity at the present moment or in the current context.
-
B.
hasNearbyFunction
chosen
Indicates that one entity has another entity located close by that serves a related or supportive function.
-
C.
nearbyFrontier
Indicates that one entity is located close to a boundary or frontier region associated with another entity.
-
D.
nearbyFeature
Indicates that one entity is located close to or in the immediate vicinity of another entity.
-
E.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
- 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_69ad85d0c5488190a3d8e02ebd01a1aa |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc6bb0748190bfccfe25d2ab41b7 |
completed | March 8, 2026, 6:14 p.m. |
| PD | Predicate disambiguation | batch_69adae121a048190b03825a001d21f49 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:19 p.m.