Triple
T1507503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara |
E33933
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Zara |
E137996
|
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: Zara | Statement: [Sara, relatedName, Zara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zara Context triple: [Sara, relatedName, Zara]
-
A.
Zara
chosen
Zara is the historical Italian name for the coastal Croatian city of Zadar on the Adriatic Sea.
-
B.
Uniqlo
Uniqlo is a global Japanese clothing retailer known for its affordable, minimalist casual wear and functional basics.
-
C.
Primark
Primark is a major Irish-founded fast-fashion retail chain known for its low-priced clothing, accessories, and home goods, operating under the Penneys brand in Ireland and Primark elsewhere in Europe and the United States.
-
D.
Bata
Bata is a major port city on the mainland of Equatorial Guinea, serving as a key economic and transportation hub for the country.
-
E.
Dolce & Gabbana
Dolce & Gabbana is a luxury Italian fashion house renowned for its glamorous, Mediterranean-inspired clothing, accessories, and fragrances.
- 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_69a885f352a4819099b24ff15489dede |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a8891c80a88190a813aab099abe2b0 |
completed | March 4, 2026, 7:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad23397c048190b1d2097874ed1792 |
completed | March 8, 2026, 7:20 a.m. |
Created at: March 4, 2026, 7:24 p.m.