Triple
T20602573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mortal Kombat X |
E506219
|
entity |
| Predicate | notableCharacter |
P1481
|
FINISHED |
| Object |
Ferra/Torr
Ferra/Torr is a dual-character fighter from the Mortal Kombat series, consisting of a small, agile girl and her massive brute companion who battle together as a single unit.
|
E1439625
|
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: Ferra/Torr | Statement: [Mortal Kombat X, notableCharacter, Ferra/Torr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ferra/Torr Context triple: [Mortal Kombat X, notableCharacter, Ferra/Torr]
-
A.
Ferno
Ferno is a small municipality in the Lombardy region of northern Italy, situated in the province of Varese.
-
B.
Ferden
Ferden is a small alpine municipality in the canton of Valais in southwestern Switzerland, known for its mountainous scenery and traditional Swiss village character.
-
C.
Shirocco
Shirocco was a top-class German-bred Thoroughbred racehorse and successful sire, best known for winning the 2005 Breeders’ Cup Turf.
-
D.
Ferlito
Ferlito is an Italian surname most notably associated with American actress Vanessa Ferlito.
-
E.
Tysso
Tysso is a river in the municipality of Ulvik in western Norway, known for flowing through a scenic fjord landscape.
- 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: Ferra/Torr Triple: [Mortal Kombat X, notableCharacter, Ferra/Torr]
Generated description
Ferra/Torr is a dual-character fighter from the Mortal Kombat series, consisting of a small, agile girl and her massive brute companion who battle together as a single unit.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ferra/Torr Target entity description: Ferra/Torr is a dual-character fighter from the Mortal Kombat series, consisting of a small, agile girl and her massive brute companion who battle together as a single unit.
-
A.
Ferno
Ferno is a small municipality in the Lombardy region of northern Italy, situated in the province of Varese.
-
B.
Ferden
Ferden is a small alpine municipality in the canton of Valais in southwestern Switzerland, known for its mountainous scenery and traditional Swiss village character.
-
C.
Shirocco
Shirocco was a top-class German-bred Thoroughbred racehorse and successful sire, best known for winning the 2005 Breeders’ Cup Turf.
-
D.
Ferlito
Ferlito is an Italian surname most notably associated with American actress Vanessa Ferlito.
-
E.
Tysso
Tysso is a river in the municipality of Ulvik in western Norway, known for flowing through a scenic fjord landscape.
- 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa20f5c881909265ce7d96efc487 |
completed | April 20, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08b3f35cb08190a7cdc354534fba1c |
completed | May 16, 2026, 6:14 p.m. |
| NEDg | Description generation | batch_6a08b4c5946081909ac59ed47f3ba909 |
completed | May 16, 2026, 6:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08b53450a481909133bd18c34dc78c |
completed | May 16, 2026, 6:19 p.m. |
Created at: April 16, 2026, 11:41 a.m.