Triple
T6713689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ram Mandir, Ayodhya |
E153209
|
entity |
| Predicate | lengthInMeters |
P266
|
FINISHED |
| Object | approximately 161 |
—
|
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 161 | Statement: [Ram Mandir, Ayodhya, lengthInMeters, approximately 161]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengthInMeters Context triple: [Ram Mandir, Ayodhya, lengthInMeters, approximately 161]
-
A.
approximateLengthInMeters
Indicates the estimated or roughly measured length of something expressed in meters.
-
B.
lengthInKm
Indicates that one entity specifies the length or distance of another entity measured in kilometers.
-
C.
trackLengthApproxKm
Indicates that one entity has an approximate track length, measured in kilometers, associated with it.
-
D.
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).
-
E.
length
chosen
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
- 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.