Triple
T3090223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lohri |
E64462
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Maghi |
E114662
|
NE 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: Maghi | Statement: [Lohri, relatedTo, Maghi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maghi Context triple: [Lohri, relatedTo, Maghi]
-
A.
Maghi
chosen
Maghi is a regional name for the Hindu harvest festival of Makar Sankranti, particularly observed in parts of North India.
-
B.
Dhulandi
Dhulandi is the vibrant second day of the Holi festival in India, celebrated with the playful throwing of colored powders and water.
-
C.
Murzuq
Murzuq is an oasis town in southwestern Libya that historically served as an important Saharan trade and caravan center in the Fezzan region.
-
D.
El Maasara
El Maasara is a district and suburban area in the southern part of Greater Cairo, Egypt, known for its residential neighborhoods and industrial zones.
-
E.
Masaesyli
Masaesyli was an ancient Berber kingdom in North Africa that preceded and formed part of what later became Numidia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ad857c97d88190b26f9b1c90839c77 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada20d8f788190b05b8b6b5042bc1a |
completed | March 8, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f8a6fe2081909e2bf9ee5629f017 |
completed | March 11, 2026, 11:20 p.m. |
Created at: March 8, 2026, 3:03 p.m.