Triple
T15700350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King station (Toronto) |
E380575
|
entity |
| Predicate | hasCode |
P9567
|
FINISHED |
| Object |
KING
KING is the station code for King station, a subway stop on Toronto's Line 1 Yonge–University.
|
E1171963
|
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: KING | Statement: [King station (Toronto), hasCode, KING]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KING Context triple: [King station (Toronto), hasCode, KING]
-
A.
KING
KING is a television station in Seattle, Washington, known for its local news coverage and affiliation with major U.S. broadcast networks.
-
B.
KING
KING is an American alternative R&B trio known for their lush harmonies, dreamy production, and critically acclaimed debut album "We Are KING."
-
C.
KING
KING is the stock ticker symbol for King Digital Entertainment, the video game company best known for creating the mobile puzzle game Candy Crush Saga.
-
D.
the king
The king is a vain and authoritarian monarch whom the Little Prince meets on an asteroid, symbolizing adult obsession with power and control.
-
E.
the King
The King is a con artist character in Mark Twain's "The Adventures of Huckleberry Finn," known for his elaborate scams and deceitful schemes alongside his partner, the Duke.
- 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: KING Triple: [King station (Toronto), hasCode, KING]
Generated description
KING is the station code for King station, a subway stop on Toronto's Line 1 Yonge–University.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KING Target entity description: KING is the station code for King station, a subway stop on Toronto's Line 1 Yonge–University.
-
A.
KING
KING is the stock ticker symbol for King Digital Entertainment, the video game company best known for creating the mobile puzzle game Candy Crush Saga.
-
B.
KING
KING is a television station in Seattle, Washington, known for its local news coverage and affiliation with major U.S. broadcast networks.
-
C.
KING
KING is an American alternative R&B trio known for their lush harmonies, dreamy production, and critically acclaimed debut album "We Are KING."
-
D.
the king
The king is a vain and authoritarian monarch whom the Little Prince meets on an asteroid, symbolizing adult obsession with power and control.
-
E.
the King
The King is a con artist character in Mark Twain's "The Adventures of Huckleberry Finn," known for his elaborate scams and deceitful schemes alongside his partner, the Duke.
- 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f6d71308190971c10c599da9645 |
completed | April 16, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff75756ecc8190bd2123ddfd080fd1 |
completed | May 9, 2026, 5:57 p.m. |
| NEDg | Description generation | batch_69ff763d40348190bf102da746420390 |
completed | May 9, 2026, 6 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff76ec45948190bee47609c0d2fd10 |
completed | May 9, 2026, 6:03 p.m. |
Created at: April 10, 2026, 4:45 a.m.