Triple
T268569
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christine Teigen |
E5573
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | FABLife |
E5578
|
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: FABLife | Statement: [Christine Teigen, notableWork, FABLife]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FABLife Context triple: [Christine Teigen, notableWork, FABLife]
-
A.
FABLife
chosen
FABLife is a lifestyle-focused daytime talk show that featured a panel of hosts, including Chrissy Teigen, discussing topics like fashion, beauty, food, and pop culture.
-
B.
Petit & Fritsen
Petit & Fritsen is a historic Dutch bell foundry renowned for casting church bells and carillons used in notable towers and monuments worldwide.
-
C.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
-
D.
FÜ
FÜ is the vehicle registration code used on license plates for the city of Fürth in Bavaria, Germany.
-
E.
CAF
CAF is the Confederation of African Football, the governing body for association football in Africa and one of FIFA’s six continental confederations.
- 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25dae4a0c8190a66cf6ed3889851c |
completed | Feb. 28, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a38b9082b8819099cd5e7fe3c7335f |
completed | March 1, 2026, 12:42 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.