Triple
T6713692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ram Mandir, Ayodhya |
E153209
|
entity |
| Predicate | hasMandapa |
P72825
|
FINISHED |
| Object | multiple mandapas |
—
|
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: multiple mandapas | Statement: [Ram Mandir, Ayodhya, hasMandapa, multiple mandapas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMandapa Context triple: [Ram Mandir, Ayodhya, hasMandapa, multiple mandapas]
-
A.
hasMarae
Indicates a relationship where an entity possesses, contains, or is associated with a marae (a Māori meeting place or communal space).
-
B.
hasPagoda
Indicates that one entity possesses, contains, or is characterized by the presence of a pagoda.
-
C.
hasMainHall
Indicates that an entity possesses or includes a primary or central hall as a significant internal space.
-
D.
hasTempleOf
Indicates that a location or entity possesses, contains, or is the site of a temple dedicated to a particular deity, figure, or purpose.
-
E.
hasMainHallType
Indicates the specific category or kind of main hall associated with an entity.
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d122d6cc81909bde0c94fb95f016 |
completed | March 27, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69c6d08c5d348190a29dee668c398e70 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d11fab808190b18160ff3829fcc6 |
completed | March 27, 2026, 6:49 p.m. |
Created at: March 27, 2026, 2:07 p.m.