Triple
T3535781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pedro Pascal |
E74767
|
entity |
| Predicate | characterPortrayed |
P1507
|
FINISHED |
| Object | Maxwell Lord |
E345547
|
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: Maxwell Lord | Statement: [Pedro Pascal, characterPortrayed, Maxwell Lord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maxwell Lord Context triple: [Pedro Pascal, characterPortrayed, Maxwell Lord]
-
A.
Maxwell Lord
chosen
Maxwell Lord is a powerful and morally ambiguous DC Comics businessman and manipulator often associated with the Justice League and Wonder Woman.
-
B.
Hugo Drax
Hugo Drax is the wealthy, megalomaniacal villain in the James Bond franchise who masterminds a genocidal space-based plot in the novel and film "Moonraker."
-
C.
Owen Harper
Owen Harper is a central character in the British sci-fi series "Torchwood," serving as the team's acerbic and brilliant medical officer.
-
D.
Daniel Grayson
Daniel Grayson is a central character in the TV drama "Revenge," known as the wealthy and conflicted heir of the powerful Grayson family.
-
E.
Nick Metropolis
Nick Metropolis was a pioneering physicist and computer scientist known for his work on early computers and the development of the Metropolis algorithm in statistical physics and Monte Carlo methods.
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc4c1d081908938efb71938e1a3 |
completed | March 8, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38bcf7ffc81909242cf67f5aa7b3d |
completed | March 13, 2026, 4 a.m. |
Created at: March 8, 2026, 3:20 p.m.