Triple
T7649768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brihadeeswarar Temple |
E173217
|
entity |
| Predicate | secondaryLanguageOfInscriptions |
P78564
|
FINISHED |
| Object | Sanskrit |
—
|
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: Sanskrit | Statement: [Brihadeeswarar Temple, secondaryLanguageOfInscriptions, Sanskrit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryLanguageOfInscriptions Context triple: [Brihadeeswarar Temple, secondaryLanguageOfInscriptions, Sanskrit]
-
A.
inscriptionsLanguage
Indicates that the language used in the inscriptions on an object or surface is the specified language.
-
B.
officialLanguageOfInscriptions
Indicates the language officially used in the inscriptions associated with a particular entity.
-
C.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
D.
ancientLanguages
Indicates that the related entities are languages that originated in and were used during ancient historical periods.
-
E.
inscriptionTranslation
Indicates that a provided text expresses the translated content of a specific inscription.
- 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_69c6995473348190a4f41d110d619a18 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7061cbc3c8190a917dd7e71214182 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015dd8fc8190bc5f52a12bd46209 |
completed | March 27, 2026, 10:14 p.m. |
| PDg | Predicate description generation | batch_69c7061b218c81909fff789ba4c10e58 |
completed | March 27, 2026, 10:35 p.m. |
Created at: March 27, 2026, 3:58 p.m.