Triple
T6968944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dasara in Mysuru |
E161554
|
entity |
| Predicate | palaceIlluminationBulbs |
P37175
|
FINISHED |
| Object | Approximately 100000 |
—
|
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: Approximately 100000 | Statement: [Dasara in Mysuru, palaceIlluminationBulbs, Approximately 100000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: palaceIlluminationBulbs Context triple: [Dasara in Mysuru, palaceIlluminationBulbs, Approximately 100000]
-
A.
typeOfLampsUsed
Indicates the specific kinds or categories of lamps that are utilized in a given context or system.
-
B.
hasNumberOfShamashLights
Indicates the relationship specifying how many Shamash (helper) lights are present or associated with an object or setting.
-
C.
numberOfLights
chosen
Indicates the quantity of lights associated with or present on a given entity.
-
D.
numberOfChandeliers
Indicates the quantity of chandeliers associated with a given entity or context.
-
E.
hasNumberOfMainLights
Indicates the relationship that specifies how many primary or main lights are associated with an entity.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db152b2081909271493a5d1469fb |
completed | March 27, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:30 p.m.