Triple
T9781315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KTLA |
E237379
|
entity |
| Predicate | sisterStation |
P15137
|
FINISHED |
| Object |
KXAN-TV
KXAN-TV is a television station serving the Austin, Texas market, known primarily as the local NBC affiliate.
|
E820681
|
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: KXAN-TV | Statement: [KTLA, sisterStation, KXAN-TV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KXAN-TV Context triple: [KTLA, sisterStation, KXAN-TV]
-
A.
KPHX
KPHX is the ICAO airport code for Phoenix Sky Harbor International Airport, a major commercial airport serving the Phoenix, Arizona metropolitan area.
-
B.
KXLA
KXLA is a television station based in Los Angeles, California, known for broadcasting a variety of multicultural and multilingual programming.
-
C.
KTTV
KTTV is a Los Angeles-based television station, historically known as a major outlet for sports broadcasting and local news coverage.
-
D.
KRLD-TV
KRLD-TV is a Dallas–Fort Worth television station historically known for its local news coverage and role in reporting major events such as the assassination of President John F. Kennedy.
-
E.
KFSN-TV
KFSN-TV is a local television station in Fresno, California, serving as the market’s ABC network affiliate.
- 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: KXAN-TV Triple: [KTLA, sisterStation, KXAN-TV]
Generated description
KXAN-TV is a television station serving the Austin, Texas market, known primarily as the local NBC affiliate.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KXAN-TV Target entity description: KXAN-TV is a television station serving the Austin, Texas market, known primarily as the local NBC affiliate.
-
A.
KPHX
KPHX is the ICAO airport code for Phoenix Sky Harbor International Airport, a major commercial airport serving the Phoenix, Arizona metropolitan area.
-
B.
KXLA
KXLA is a television station based in Los Angeles, California, known for broadcasting a variety of multicultural and multilingual programming.
-
C.
KTTV
KTTV is a Los Angeles-based television station, historically known as a major outlet for sports broadcasting and local news coverage.
-
D.
KRLD-TV
KRLD-TV is a Dallas–Fort Worth television station historically known for its local news coverage and role in reporting major events such as the assassination of President John F. Kennedy.
-
E.
KFSN-TV
KFSN-TV is a local television station in Fresno, California, serving as the market’s ABC network affiliate.
- 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_69ca84da927881909bda80caecad6010 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda1b23cb88190b458ab18d5f7f493 |
completed | April 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bd2e5f4c81908a3c132df6440947 |
completed | April 5, 2026, 1:38 a.m. |
| NEDg | Description generation | batch_69d1bf4a414c81909ee12092315e714a |
completed | April 5, 2026, 1:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1c00dac1c81908c6c5dca384eddcc |
completed | April 5, 2026, 1:51 a.m. |
Created at: March 30, 2026, 8:27 p.m.