Triple
T13532345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tampa Tarpons |
E323164
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | TAR |
E323164
|
NE FINISHED |
How this triple was built (2 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: TAR | Statement: [Tampa Tarpons, abbreviation, TAR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TAR Context triple: [Tampa Tarpons, abbreviation, TAR]
-
A.
TAR
TAR is the ICAO airline designator assigned to Tunisair, the national flag carrier of Tunisia.
-
B.
TAR
TAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s Third Assessment Report on climate change.
-
C.
TAR
chosen
TAR is the standard abbreviation for the Tampa Tarpons, a Minor League Baseball team based in Tampa, Florida.
-
D.
TAR
TAR is a Mexican regional airline operating domestic passenger flights to various destinations across the country.
-
E.
TAR
TAR is the ICAO airline designator assigned to Tarco Aviation, a Sudanese carrier operating passenger and cargo flights.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafbb34548190a6b44faa48125cd4 |
completed | April 12, 2026, 2:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d95ce008190a915fe5865c38ee5 |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:44 p.m.