Triple
T32306246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hsinbyume Pagoda |
E825377
|
entity |
| Predicate | hasTerracesCount |
P23536
|
FINISHED |
| Object | seven |
—
|
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: seven | Statement: [Hsinbyume Pagoda, hasTerracesCount, seven]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTerracesCount Context triple: [Hsinbyume Pagoda, hasTerracesCount, seven]
-
A.
numberOfTerraces
chosen
Indicates the total count of terraces associated with a given entity.
-
B.
hasTerrace
Indicates that one entity includes, features, or is equipped with a terrace as part of its structure or property.
-
C.
hasTerraceComplex
Indicates that one entity possesses or includes a terrace that is part of a larger, interconnected terrace structure or complex.
-
D.
hasTerracedWalls
Indicates that an entity possesses walls that are constructed in stepped or terraced levels rather than as a single continuous vertical surface.
-
E.
hasFiveTerracesNamed
Indicates that an entity possesses exactly five terraces, each of which has a specific name.
- 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_69f349115304819084ee91d345b6c8aa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: May 1, 2026, 12:45 a.m.