Triple
T6713690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ram Mandir, Ayodhya |
E153209
|
entity |
| Predicate | widthInMeters |
P619
|
FINISHED |
| Object | approximately 128 |
—
|
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 128 | Statement: [Ram Mandir, Ayodhya, widthInMeters, approximately 128]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: widthInMeters Context triple: [Ram Mandir, Ayodhya, widthInMeters, approximately 128]
-
A.
approximateLengthInMeters
Indicates the estimated or roughly measured length of something expressed in meters.
-
B.
depthMetresApprox
Indicates an approximate measurement of how deep something is in metres, rather than an exact value.
-
C.
dimensions_km
Indicates the physical size or extent of something measured in kilometers, typically specifying one or more linear dimensions (e.g., length, width, height).
-
D.
width
chosen
Indicates the measurement of how wide an entity is, typically the extent of its horizontal dimension from side to side.
-
E.
lengthInKm
Indicates that one entity specifies the length or distance of another entity measured in kilometers.
- 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_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. |
Created at: March 27, 2026, 2:07 p.m.