Triple
T20697598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arakko (Mars) |
E508696
|
entity |
| Predicate | terraformedBy |
P141135
|
FINISHED |
| Object |
Iceman
Iceman is a powerful Omega-level mutant from the X-Men whose control over ice and temperature has grown to near-cosmic scales in recent Marvel comics.
|
E548065
|
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: Iceman | Statement: [Arakko (Mars), terraformedBy, Iceman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iceman Context triple: [Arakko (Mars), terraformedBy, Iceman]
-
A.
Iceman
Iceman is the nickname of Adam Vinatieri, the legendary NFL placekicker renowned for his clutch, game-winning field goals under extreme pressure.
-
B.
Iceman
Iceman is a founding member of the X-Men with the mutant ability to generate and control ice and cold.
-
C.
Iceman
Iceman is a 1984 science fiction drama film about the discovery and revival of a prehistoric man frozen in ice.
-
D.
Iceman
Iceman is the call sign of Tom Kazansky, a skilled and competitive U.S. Navy fighter pilot from the "Top Gun" film series.
-
E.
Iceman
Iceman is a 2014 Hong Kong action-comedy film starring Donnie Yen as a Ming Dynasty warrior who time-travels to modern-day Hong Kong.
- 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: Iceman Triple: [Arakko (Mars), terraformedBy, Iceman]
Generated description
Iceman is a powerful Omega-level mutant from the X-Men whose control over ice and temperature has grown to near-cosmic scales in recent Marvel comics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Iceman Target entity description: Iceman is a powerful Omega-level mutant from the X-Men whose control over ice and temperature has grown to near-cosmic scales in recent Marvel comics.
-
A.
Iceman
Iceman is the nickname of Adam Vinatieri, the legendary NFL placekicker renowned for his clutch, game-winning field goals under extreme pressure.
-
B.
Iceman
chosen
Iceman is a founding member of the X-Men with the mutant ability to generate and control ice and cold.
-
C.
Iceman
Iceman is a 1984 science fiction drama film about the discovery and revival of a prehistoric man frozen in ice.
-
D.
Iceman
Iceman is the call sign of Tom Kazansky, a skilled and competitive U.S. Navy fighter pilot from the "Top Gun" film series.
-
E.
Iceman
Iceman is a 2014 Hong Kong action-comedy film starring Donnie Yen as a Ming Dynasty warrior who time-travels to modern-day Hong Kong.
- F. None of above.
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_69e0b4c2b2a481909e31e9cb8f81ab55 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c1132178819081086e085f7b3ff8 |
completed | April 21, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08e82c317c8190b4947e8d32494074 |
completed | May 16, 2026, 9:57 p.m. |
| NEDg | Description generation | batch_6a08e8aaf8c48190a39ff61647931670 |
completed | May 16, 2026, 9:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08e91102e081909b22c8d69c6c994c |
completed | May 16, 2026, 10 p.m. |
Created at: April 16, 2026, 12:11 p.m.