Triple
T706156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto Maple Leafs |
E14103
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
TOR
TOR is the standard three-letter abbreviation used to represent the Toronto Maple Leafs in sports standings, statistics, and media.
|
E84202
|
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: TOR | Statement: [Toronto Maple Leafs, abbreviation, TOR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TOR Context triple: [Toronto Maple Leafs, abbreviation, TOR]
-
A.
TER
TER is a network of regional express trains in France that provides local passenger rail services across various regions.
-
B.
Tur
Tur is a foundational 14th-century Jewish legal code by Rabbi Jacob ben Asher that systematically organized halakhic rulings and served as a primary basis for later works like the Shulchan Aruch.
-
C.
TU
TU is the international vehicle registration code assigned to Tunisia.
-
D.
TW
TW is the two-letter ISO 3166 country code assigned to Taiwan (commonly referred to as Chinese Taipei in certain international contexts).
-
E.
TR
TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
- 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: TOR Triple: [Toronto Maple Leafs, abbreviation, TOR]
Generated description
TOR is the standard three-letter abbreviation used to represent the Toronto Maple Leafs in sports standings, statistics, and media.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TOR Target entity description: TOR is the standard three-letter abbreviation used to represent the Toronto Maple Leafs in sports standings, statistics, and media.
-
A.
TER
TER is a network of regional express trains in France that provides local passenger rail services across various regions.
-
B.
Tur
Tur is a foundational 14th-century Jewish legal code by Rabbi Jacob ben Asher that systematically organized halakhic rulings and served as a primary basis for later works like the Shulchan Aruch.
-
C.
TU
TU is the international vehicle registration code assigned to Tunisia.
-
D.
TW
TW is the two-letter ISO 3166 country code assigned to Taiwan (commonly referred to as Chinese Taipei in certain international contexts).
-
E.
TR
TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
- 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a54607f08190b3ee4805f2ea4b2f |
completed | March 1, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5dcb1795c8190a178e14509b8b271 |
completed | March 2, 2026, 6:53 p.m. |
| NEDg | Description generation | batch_69a5de4387f081909fc3f7c7db03a375 |
completed | March 2, 2026, 7 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5ff6bcbd8819089f7a303a6a491a8 |
completed | March 2, 2026, 9:21 p.m. |
Created at: March 1, 2026, 7:36 p.m.