Triple
T7080734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teterboro Airport |
E164946
|
entity |
| Predicate | IATA code |
P2569
|
FINISHED |
| Object |
TEB
TEB is the IATA airport code for Teterboro Airport, a major general aviation and corporate jet airport serving the New York City metropolitan area in New Jersey.
|
E641183
|
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: TEB | Statement: [Teterboro Airport, IATA code, TEB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TEB Context triple: [Teterboro Airport, IATA code, TEB]
-
A.
ETB
ETB is the three-letter international currency code used to represent the Ethiopian birr in global financial and foreign exchange contexts.
-
B.
TEC
TEC is a public transport company in Belgium that operates regional bus and other transit services, primarily in the Walloon region.
-
C.
TEC
TEC is the commonly used acronym for the Episcopal Church, a mainline Anglican Christian denomination based in the United States.
-
D.
TIB
TIB is a major German academic institution and library specializing in science and technology information, including digital and research data services.
-
E.
TEP
TEP is the stock ticker symbol for Teleperformance, a global leader in outsourced customer experience management and business process outsourcing services.
- 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: TEB Triple: [Teterboro Airport, IATA code, TEB]
Generated description
TEB is the IATA airport code for Teterboro Airport, a major general aviation and corporate jet airport serving the New York City metropolitan area in New Jersey.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TEB Target entity description: TEB is the IATA airport code for Teterboro Airport, a major general aviation and corporate jet airport serving the New York City metropolitan area in New Jersey.
-
A.
ETB
ETB is the three-letter international currency code used to represent the Ethiopian birr in global financial and foreign exchange contexts.
-
B.
TEC
TEC is a public transport company in Belgium that operates regional bus and other transit services, primarily in the Walloon region.
-
C.
TEC
TEC is the commonly used acronym for the Episcopal Church, a mainline Anglican Christian denomination based in the United States.
-
D.
TIB
TIB is a major German academic institution and library specializing in science and technology information, including digital and research data services.
-
E.
TEP
TEP is the stock ticker symbol for Teleperformance, a global leader in outsourced customer experience management and business process outsourcing services.
- 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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4f063488190b9e1c614a9294bd1 |
completed | March 27, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79477a79c81909b51175a24d17142 |
completed | March 28, 2026, 8:42 a.m. |
| NEDg | Description generation | batch_69c798cd9e4c8190a9dc1176d60d2cf6 |
completed | March 28, 2026, 9:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c799fae8608190b0cd94d59ab589a4 |
completed | March 28, 2026, 9:06 a.m. |
Created at: March 27, 2026, 2:40 p.m.