Triple
T14801417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | موتی مسجد (لال قلعہ، دہلی) |
E347918
|
entity |
| Predicate | تعمیراتی طرز |
P115770
|
FINISHED |
| Object | مغلیہ طرزِ تعمیر |
—
|
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: مغلیہ طرزِ تعمیر | Statement: [موتی مسجد (لال قلعہ، دہلی), تعمیراتی طرز, مغلیہ طرزِ تعمیر]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: تعمیراتی طرز Context triple: [موتی مسجد (لال قلعہ، دہلی), تعمیراتی طرز, مغلیہ طرزِ تعمیر]
-
A.
restorationType
Indicates the specific kind or category of restoration applied to an entity, such as the method, scope, or approach used to return it to a prior or improved state.
-
B.
reparationsType
Indicates the specific category or form of reparations involved in a reparative action or obligation between entities.
-
C.
repairedIn
Indicates that an item or object underwent repair within a specified location or during a particular time period.
-
D.
maintenance
Indicates that an entity performs, requires, or is involved in upkeep, repair, or preservation activities for another entity or system.
-
E.
maintenancePractice
Indicates the specific actions or methods used to preserve, repair, or optimize the condition or performance of something over time.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decd62c36c81909c2993dc7d1a79ea |
completed | April 14, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69de8c0ef8a4819092d84478b1f56db1 |
completed | April 14, 2026, 6:48 p.m. |
| PDg | Predicate description generation | batch_69de8f4b67cc8190b84b59fcec5cf579 |
completed | April 14, 2026, 7:02 p.m. |
Created at: April 10, 2026, 1:31 a.m.