Triple
T8628255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aquaman and the Lost Kingdom |
E204332
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Mera |
E203763
|
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: Mera | Statement: [Aquaman and the Lost Kingdom, mainCharacter, Mera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mera Context triple: [Aquaman and the Lost Kingdom, mainCharacter, Mera]
-
A.
Mera
chosen
Mera is a powerful Atlantean warrior and sorceress from DC Comics, best known as Aquaman’s ally and queen of Atlantis.
-
B.
Mekhu
Mekhu was an ancient Egyptian official and noble whose rock-cut tomb is located among the Tombs of the Nobles at Aswan.
-
C.
Uma
Uma is a central antagonist in Disney's "Descendants" franchise, known as the ambitious and strong-willed daughter of Ursula who leads a pirate crew on the Isle of the Lost.
-
D.
Uma
Uma is an Austronesian language spoken primarily in Central Sulawesi, Indonesia.
-
E.
Uma
Uma is a Bengali film directed by Srijit Mukherji, inspired by a real-life story of a terminally ill girl whose father recreates the Durga Puja festival early so she can experience it.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc472ccc0c81908e0708c94a7cbe65 |
completed | March 31, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebc02b4008190a70ca8eb6f43926d |
completed | April 2, 2026, 6:57 p.m. |
Created at: March 30, 2026, 6:27 p.m.