Triple
T15068120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florence Knoll |
E379805
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Shu |
E287878
|
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: Shu | Statement: [Florence Knoll, alsoKnownAs, Shu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shu Context triple: [Florence Knoll, alsoKnownAs, Shu]
-
A.
Shu
chosen
Shu is a Chinese surname borne by numerous individuals across history and contemporary society.
-
B.
Shu
Shu is an ancient Egyptian god of air and light, often depicted separating the sky goddess Nut from the earth god Geb.
-
C.
Shen
Shen is a Chinese surname historically borne by notable figures such as the Song dynasty polymath Shen Kuo.
-
D.
Shinjo
Shinjo is a notable figure associated with the Kegon school of Japanese Buddhism, recognized for contributions to its teachings or development.
-
E.
Tsu
Tsu is a coastal city in central Japan that serves as the administrative and economic center of Mie Prefecture.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedeebc7e48190a86b4f0afe8844bb |
completed | April 15, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5cb04e88190a42bb0e516df61bc |
completed | May 9, 2026, 3:11 a.m. |
Created at: April 10, 2026, 3:02 a.m.