Triple
T4129288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Six Flags Great Adventure |
E85000
|
entity |
| Predicate | hasRollerCoaster |
P23566
|
FINISHED |
| Object |
Green Lantern
Green Lantern is a stand-up steel roller coaster at Six Flags Great Adventure themed after the DC Comics superhero.
|
E416175
|
NE FINISHED |
How this triple was built (4 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: [Six Flags Great Adventure, hasRollerCoaster, Green Lantern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green Lantern Context triple: [Six Flags Great Adventure, hasRollerCoaster, Green Lantern]
-
A.
Green Lantern
Green Lantern is a long-running DC Comics superhero franchise centered on intergalactic peacekeepers who wield power rings fueled by willpower.
-
B.
Mister Miracle
Mister Miracle is a DC Comics superhero and master escape artist created by Jack Kirby as part of his Fourth World saga.
-
C.
The Peacemaker
The Peacemaker is the legendary spiritual leader credited with uniting the Haudenosaunee (Iroquois) nations under the Great Law of Peace.
-
D.
Legion
"Legion" is a 2010 supernatural action-horror film in which archangel Michael defies God to protect humanity from an impending apocalypse.
-
E.
Legion
Legion is Lenovo's gaming-focused brand of high-performance laptops, desktops, and related PC hardware.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Green Lantern Triple: [Six Flags Great Adventure, hasRollerCoaster, Green Lantern]
Generated description
Green Lantern is a stand-up steel roller coaster at Six Flags Great Adventure themed after the DC Comics superhero.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Green Lantern Target entity description: Green Lantern is a stand-up steel roller coaster at Six Flags Great Adventure themed after the DC Comics superhero.
-
A.
Green Lantern
Green Lantern is a long-running DC Comics superhero franchise centered on intergalactic peacekeepers who wield power rings fueled by willpower.
-
B.
Mister Miracle
Mister Miracle is a DC Comics superhero and master escape artist created by Jack Kirby as part of his Fourth World saga.
-
C.
The Peacemaker
The Peacemaker is the legendary spiritual leader credited with uniting the Haudenosaunee (Iroquois) nations under the Great Law of Peace.
-
D.
Legion
"Legion" is a 2010 supernatural action-horror film in which archangel Michael defies God to protect humanity from an impending apocalypse.
-
E.
Legion
Legion is Lenovo's gaming-focused brand of high-performance laptops, desktops, and related PC hardware.
- F. None of above. chosen
Provenance (5 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af021c5ca48190a829bab07dda55d0 |
completed | March 9, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b576bf503c8190be44139a908ee42d |
completed | March 14, 2026, 2:54 p.m. |
| NEDg | Description generation | batch_69b577ac31888190b6182b00bd5c709f |
completed | March 14, 2026, 2:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b57839ee548190804ef306fc9b3a6e |
completed | March 14, 2026, 3:01 p.m. |
Created at: March 9, 2026, 3:42 p.m.