Triple
T7508210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tedy Bruschi |
E177446
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tedy |
E177446
|
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: Tedy | Statement: [Tedy Bruschi, givenName, Tedy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tedy Context triple: [Tedy Bruschi, givenName, Tedy]
-
A.
Tedy
chosen
Tedy is the given name of Tedy Bruschi, a former professional American football linebacker best known for his career with the New England Patriots.
-
B.
Teda
Teda are a Saharan ethnic group, primarily inhabiting northern Chad and surrounding regions, known for their nomadic lifestyle and Tebu language.
-
C.
Tede
Tede is an archaeological site in Nigeria notable for yielding significant Ife bronze and terracotta sculptures associated with the ancient Yoruba civilization.
-
D.
Tále
Tále is a popular ski resort and recreational area in the Low Tatras mountains of central Slovakia, known for its slopes, golf course, and year-round outdoor activities.
-
E.
Tverya
Tverya is the Hebrew name for Tiberias, an ancient city in northern Israel on the western shore of the Sea of Galilee known for its religious significance and hot springs.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b8ab5c8190828ee8d144068828 |
completed | March 27, 2026, 9:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83ca56e3c81908c3bae8ad2d9ecd1 |
completed | March 28, 2026, 8:40 p.m. |
Created at: March 27, 2026, 3:45 p.m.