Triple
T11349779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pyrénées-Orientales |
E268810
|
entity |
| Predicate | hasHighestPoint |
P210
|
FINISHED |
| Object |
Pic Carlit
Pic Carlit is a prominent mountain peak in the eastern French Pyrenees, popular with hikers for its panoramic views over numerous surrounding lakes.
|
E920258
|
NE FINISHED |
How this triple was built (4 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: Pic Carlit | Statement: [Pyrénées-Orientales, hasHighestPoint, Pic Carlit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pic Carlit Context triple: [Pyrénées-Orientales, hasHighestPoint, Pic Carlit]
-
A.
Pete
Pete is a common masculine given name, typically used as a familiar or informal form of the name Peter.
-
B.
Pete
Pete is a classic Disney cartoon villain, best known as Mickey Mouse’s burly, antagonistic foe in the Mickey Mouse franchise.
-
C.
Pete
Pete is a fictional character known as the son of Uncle Tom in Harriet Beecher Stowe’s anti-slavery novel "Uncle Tom’s Cabin."
-
D.
Pete
Pete is the nickname of Grover Cleveland Alexander, a Hall of Fame Major League Baseball pitcher and one of the greatest hurlers of the early 20th century.
-
E.
Pete
Pete is a central figure in Stephen Crane’s novella "Maggie: A Girl of the Streets," representing the rough, working-class masculinity of New York’s Bowery slums.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Pic Carlit Triple: [Pyrénées-Orientales, hasHighestPoint, Pic Carlit]
Generated description
Pic Carlit is a prominent mountain peak in the eastern French Pyrenees, popular with hikers for its panoramic views over numerous surrounding lakes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pic Carlit Target entity description: Pic Carlit is a prominent mountain peak in the eastern French Pyrenees, popular with hikers for its panoramic views over numerous surrounding lakes.
-
A.
Pete
Pete is a common masculine given name, typically used as a familiar or informal form of the name Peter.
-
B.
Pete
Pete is a classic Disney cartoon villain, best known as Mickey Mouse’s burly, antagonistic foe in the Mickey Mouse franchise.
-
C.
Pete
Pete is a fictional character known as the son of Uncle Tom in Harriet Beecher Stowe’s anti-slavery novel "Uncle Tom’s Cabin."
-
D.
Pete
Pete is the nickname of Grover Cleveland Alexander, a Hall of Fame Major League Baseball pitcher and one of the greatest hurlers of the early 20th century.
-
E.
Pete
Pete is a central figure in Stephen Crane’s novella "Maggie: A Girl of the Streets," representing the rough, working-class masculinity of New York’s Bowery slums.
- F. None of above. chosen
Provenance (5 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_69d6aacbe18081909e5fadb50082dd96 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea23391c819089e8f9725cb3a0ff |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5439a7ee481908d244f79041b3af6 |
completed | April 19, 2026, 9:05 p.m. |
| NEDg | Description generation | batch_69e5474b77948190b2c45831871383e8 |
completed | April 19, 2026, 9:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e54eeba4a88190af128a99c277853a |
completed | April 19, 2026, 9:53 p.m. |
Created at: April 8, 2026, 9:33 p.m.