Triple
T10368779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dave Gibbons |
E244320
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Green Lantern |
E101339
|
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: Green Lantern | Statement: [Dave Gibbons, notableWork, Green Lantern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green Lantern Context triple: [Dave Gibbons, notableWork, Green Lantern]
-
A.
Green Lantern
chosen
Green Lantern is a long-running DC Comics superhero franchise centered on intergalactic peacekeepers who wield power rings fueled by willpower.
-
B.
Green Lantern
Green Lantern is a stand-up steel roller coaster at Six Flags Great Adventure themed after the DC Comics superhero.
-
C.
Mister Miracle
Mister Miracle is a DC Comics superhero and master escape artist created by Jack Kirby as part of his Fourth World saga.
-
D.
Guardian of the Universe
Guardian of the Universe is an epithet for Gamera, the giant flying turtle kaiju from Japanese films who protects humanity from monstrous threats.
-
E.
The Peacemaker
The Peacemaker is a 1997 American action-thriller film about U.S. efforts to prevent nuclear terrorism, notable as the first film released by DreamWorks Pictures.
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e97106448190a075948e63184f47 |
completed | April 7, 2026, 11:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d79550b2ec8190ada086ddfeb398af |
completed | April 9, 2026, 12:02 p.m. |
Created at: April 6, 2026, 12:01 p.m.