Triple
T15356348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guardians of the Galaxy Vol. 2 |
E367175
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Pom Klementieff |
E256909
|
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: Pom Klementieff | Statement: [Guardians of the Galaxy Vol. 2, stars, Pom Klementieff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pom Klementieff Context triple: [Guardians of the Galaxy Vol. 2, stars, Pom Klementieff]
-
A.
Pom Klementieff
chosen
Pom Klementieff is a French actress best known for playing Mantis in the Marvel Cinematic Universe films.
-
B.
Nikita Dragun
Nikita Dragun is a transgender beauty influencer, YouTuber, and entrepreneur known for her makeup content and cosmetics brand Dragun Beauty.
-
C.
Jessica Soho
Jessica Soho is a prominent Filipino broadcast journalist and television news anchor renowned for her in-depth documentaries and long-running public affairs programs.
-
D.
Tasya Vos
Tasya Vos is the emotionally detached, body-hopping assassin protagonist of Brandon Cronenberg’s 2020 science fiction horror film "Possessor."
-
E.
Svetlana Khodchenkova
Svetlana Khodchenkova is a Russian film and television actress known internationally for roles in movies such as "Tinker Tailor Soldier Spy" and "The Wolverine."
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2c00648190ae2325e1ee58dcfd |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff02012fa48190a108f1ca710ffb15 |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:18 a.m.