Triple
T14005085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arjumand Banu Begum |
E336925
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Taj Mahal |
E12234
|
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: Taj Mahal | Statement: [Arjumand Banu Begum, associatedWith, Taj Mahal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taj Mahal Context triple: [Arjumand Banu Begum, associatedWith, Taj Mahal]
-
A.
Taj Mahal
chosen
The Taj Mahal is a 17th-century white marble mausoleum in Agra, India, renowned worldwide as a masterpiece of Mughal architecture and a symbol of enduring love.
-
B.
Taj Mahal
Taj Mahal is an American blues musician and singer-songwriter known for blending traditional blues with elements of world music, including Caribbean, African, and Hawaiian influences.
-
C.
Taj
Taj was the former name of Esteghlal F.C., one of Iran’s most successful and popular football clubs.
-
D.
Taj
Taj is an American actor and musician best known as the son of Aerosmith frontman Steven Tyler.
-
E.
Taj
Taj is a luxury hospitality brand known for its upscale hotels, resorts, and palaces operated by Taj Hotels.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed1d2548190bb46d6b7cba4ffde |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd095ca5081908d7fed82e9ef0252 |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:19 p.m.