Triple
T20104397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TNGHT |
E184899
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Serpent |
—
|
NE NERFINISHED |
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: Serpent | Statement: [TNGHT, notableWork, Serpent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serpent Context triple: [TNGHT, notableWork, Serpent]
-
A.
Serpent
chosen
Serpent is a symmetric block cipher designed as a highly secure, conservative candidate for the Advanced Encryption Standard (AES) competition.
-
B.
Basilisk
The Basilisk is a gigantic, deadly serpent from the Harry Potter series whose gaze can kill and whose venom is among the most lethal magical substances.
-
C.
Black Snake
Black Snake is a film production company associated with the creation of the independent movie "Down by Law."
-
D.
Serpent King
Serpent King is a regal epithet of Vasuki, the mighty naga ruler in Hindu mythology who serves as the cosmic serpent used as a rope in the churning of the ocean.
-
E.
Schlangen
Schlangen is a small municipality in the Lippe district of North Rhine-Westphalia, Germany, known for its rural character and proximity to the Teutoburg Forest.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666daf73c819089f02ca6faa2c283 |
completed | April 20, 2026, 5:48 p.m. |
Created at: April 11, 2026, 11:27 p.m.