Triple
T3066280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Beauty |
E62110
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Tariq Anwar |
E10358
|
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: Tariq Anwar | Statement: [American Beauty, editor, Tariq Anwar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tariq Anwar Context triple: [American Beauty, editor, Tariq Anwar]
-
A.
Tariq Anwar
chosen
Tariq Anwar is a British film editor known for his acclaimed work on numerous major films, including the Academy Award–winning drama "The King’s Speech."
-
B.
Javid Iqbal
Javid Iqbal was a Pakistani jurist, philosopher, and senior judge of the Supreme Court of Pakistan, known also as the son of poet-philosopher Allama Muhammad Iqbal.
-
C.
Salim Malik
Salim Malik is a central character in the film "Slumdog Millionaire," portrayed as the conflicted older brother of the protagonist Jamal whose choices drive much of the story’s drama and moral tension.
-
D.
Hasnat Khan
Hasnat Khan is a British-Pakistani heart surgeon best known for his romantic relationship with Diana, Princess of Wales.
-
E.
Syed Iftikar
Syed Iftikar is an entrepreneur best known as a co-founder of the data storage company Seagate Technology.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fd87308190918e7b616f033faa |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ef16cf2881908265dfe8a1e3424d |
completed | March 11, 2026, 10:39 p.m. |
Created at: March 8, 2026, 3:02 p.m.