Triple
T18849823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisbon Zoo |
E461010
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Sete Rios |
—
|
NE NERFINISHED |
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: Sete Rios | Statement: [Lisbon Zoo, locatedIn, Sete Rios]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sete Rios Context triple: [Lisbon Zoo, locatedIn, Sete Rios]
-
A.
Sete Rios
chosen
Sete Rios is a major transport hub and neighborhood in Lisbon, Portugal, known for its bus and train connections and proximity to the city zoo.
-
B.
Rioverde
Rioverde is a city in the state of San Luis Potosí, Mexico, known as a regional agricultural and commercial center in the country's central highlands.
-
C.
Araxa
Araxa was an ancient city in the region of Lycia in southwestern Anatolia, known as one of the smaller members of the Lycian League.
-
D.
Río
Río is a central character in the Spanish television series "La Casa de Papel" ("Money Heist"), known as a young, talented hacker and member of the Professor's heist crew.
-
E.
Pasión River
The Pasión River is a significant river in Guatemala that drains part of the Petén region and ultimately contributes its waters to the larger Usumacinta River system.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfa11e4819090ab1ef5bdcd2b2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5b8f1abdc819081638ed384da191e |
completed | April 20, 2026, 5:26 a.m. |
Created at: April 10, 2026, 11:56 a.m.