Triple
T14484940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yara Greyjoy |
E359202
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Yara |
E695154
|
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: Yara | Statement: [Yara Greyjoy, givenName, Yara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yara Context triple: [Yara Greyjoy, givenName, Yara]
-
A.
Yara
chosen
Yara is a municipality in eastern Cuba known for its historical significance in the country’s struggle for independence.
-
B.
Tona
Tona is a municipality in the comarca of Osona in Catalonia, Spain, known for its rural character and proximity to the city of Vic.
-
C.
Zea Marina
Zea Marina is a prominent yacht and leisure marina in Piraeus, Greece, known for its modern facilities and proximity to Athens.
-
D.
Nauta
Nauta is a small river port town in Peru’s Loreto region, serving as a key gateway for tourism and access to the Pacaya-Samiria National Reserve in the Amazon rainforest.
-
E.
Soroa
Soroa is a small Cuban village and popular ecotourism destination known for its lush mountain scenery, waterfalls, and orchid garden.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de924d7f4c8190b1f62b5ffe1ff649 |
completed | April 14, 2026, 7:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd64a73cf48190811d6de182e891c4 |
completed | May 8, 2026, 4:20 a.m. |
Created at: April 10, 2026, 1:20 a.m.