Triple
T3974699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston T |
E85611
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | the T |
E16690
|
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: the T | Statement: [Boston T, shortName, the T]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: the T Context triple: [Boston T, shortName, the T]
-
A.
the T
chosen
The T is the public transit system serving the Greater Boston area, operated by the Massachusetts Bay Transportation Authority.
-
B.
T
T is the vehicle registration code used for motor vehicles registered in Troms county in Norway.
-
C.
T
T is the New York Stock Exchange ticker symbol for AT&T Inc., a major American telecommunications and media company.
-
D.
tet
tet is the ISO 639-1 language code for Tetum, an Austronesian language spoken primarily in East Timor.
-
E.
TA
TA is a common abbreviation for the Territorial Army, a volunteer reserve force that supports a country's regular armed forces.
- 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_69aed93908348190a26c8aaf4fab3e86 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef9b511f88190afca12c77481b344 |
completed | March 9, 2026, 4:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5401712188190aa6144dc1d5dcab6 |
completed | March 14, 2026, 11:01 a.m. |
Created at: March 9, 2026, 3:32 p.m.